{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0101aa7c-3689-4022-9780-33310f9a1d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n",
      "x_train.shape= (25000,)\n",
      "y_train.shape= (25000,)\n",
      "x_test.shape= (25000,)\n",
      "y_test.shape= (25000,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "imdb=tf.keras.datasets.imdb\n",
    "(x_train,y_train),(x_test,y_test)=imdb.load_data(num_words=4000)\n",
    "print(\"x_train.shape=\",x_train.shape)\n",
    "print(\"y_train.shape=\",y_train.shape)\n",
    "print(\"x_test.shape=\",x_test.shape)\n",
    "print(\"y_test.shape=\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80914155-7f12-4b4c-8985-ecc15b25e6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "序列填充前的第一个元素：\n",
      " [1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 2, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
      "序列填充后的第一个元素：\n",
      " [   1   14   22   16   43  530  973 1622 1385   65  458    2   66 3941\n",
      "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
      "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
      "  172    2 1111   17  546   38   13  447    4  192   50   16    6  147\n",
      " 2025   19   14   22    4 1920    2  469    4   22   71   87   12   16\n",
      "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
      "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
      "    2   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
      "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
      "   52    5   14  407   16   82    2    8    4  107  117    2   15  256\n",
      "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
      "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
      " 2071   56   26  141    6  194    2   18    4  226   22   21  134  476\n",
      "   26  480    5  144   30    2   18   51   36   28  224   92   25  104\n",
      "    4  226   65   16   38 1334   88   12   16  283    5   16    2  113\n",
      "  103   32   15   16    2   19  178   32    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0]\n"
     ]
    }
   ],
   "source": [
    "print(\"序列填充前的第一个元素：\\n\",x_train[0])\n",
    "x_train=tf.keras.preprocessing.sequence.pad_sequences(x_train,padding='post',maxlen=400,truncating='post')\n",
    "x_test=tf.keras.preprocessing.sequence.pad_sequences(x_test,padding='post',maxlen=400,truncating='post')\n",
    "print(\"序列填充后的第一个元素：\\n\",x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a21d713d-7439-4976-9e33-ecb554e7557e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(output_dim=32,input_dim=4000,input_length=400))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.LSTM(32))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b2cc741-e9ae-454e-a0a2-f0a6015cc3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 75ms/step - accuracy: 0.5067 - loss: 0.6933 - val_accuracy: 0.4938 - val_loss: 0.6936\n",
      "Epoch 2/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 73ms/step - accuracy: 0.5107 - loss: 0.6925 - val_accuracy: 0.5008 - val_loss: 0.6941\n",
      "Epoch 3/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 73ms/step - accuracy: 0.5120 - loss: 0.6919 - val_accuracy: 0.5016 - val_loss: 0.6938\n",
      "Epoch 4/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 73ms/step - accuracy: 0.5074 - loss: 0.6915 - val_accuracy: 0.5020 - val_loss: 0.6944\n",
      "Epoch 5/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 72ms/step - accuracy: 0.5135 - loss: 0.6913 - val_accuracy: 0.4960 - val_loss: 0.6935\n",
      "Epoch 6/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 74ms/step - accuracy: 0.5178 - loss: 0.6899 - val_accuracy: 0.5070 - val_loss: 0.6930\n",
      "Epoch 7/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.5244 - loss: 0.6879 - val_accuracy: 0.5024 - val_loss: 0.6924\n",
      "Epoch 8/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 80ms/step - accuracy: 0.5217 - loss: 0.6860 - val_accuracy: 0.5256 - val_loss: 0.6885\n",
      "Epoch 9/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 82ms/step - accuracy: 0.5297 - loss: 0.6824 - val_accuracy: 0.5344 - val_loss: 0.6827\n",
      "Epoch 10/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 80ms/step - accuracy: 0.5441 - loss: 0.6788 - val_accuracy: 0.5246 - val_loss: 0.6847\n",
      "391/391 - 9s - 23ms/step - accuracy: 0.5172 - loss: 0.6876\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6875860095024109, 0.5172399878501892]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)\n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60d7f966-9638-4577-80e9-51d267714fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xr57ra8ybNw/Lli3Dzz//jDVr1mDo0KF4/PgxKnLFXsZKjLS0zJL6L1/SOkdHYOVKGgGjTDhJU3ZCAM+eZU/G7tzJHNX4IW1t6lZXv75iMmZhoTwtSXp6VFyjRo2Pb5eWlnvrXHo6JWMtW9K4L1WYN0Ymo/L+TZsC7u40R5uXFyVtT58Cu3fTYmBA1SEHDqTy/jzxLGOsFDMwMICWlhbKlCkDY2NjAMDdu3cBAPPnz0eXLl3k21aqVAmNGjWS31+4cCEOHDiAw4cPY8KECbm+xogRI/D1118DABYvXow1a9bgypUr6FZc9bgZY5/l/HkqqX/9Ot2vU4dK6nftKmlYueIkTVm9ekWp/Z07QGxszttoatI4sXr1FBOyGjWUs8JiQairU0kdIyMgy0m1VJDJKAlt1gxYtoy6du7bR6X9IyKAHTtoqVCBJsweNIgm0C7swieMsVKrTJmcay4V12sXBjs7O4X779+/x7x583DkyBE8f/4cqampSEhIwJMnTz66n4ZZeojo6elBX18fURmz2zLGlFZEBBXb3r2b7pcrB8ydC0yYoNxfmVTkm7wKqlABuHGDCnVoaFBL0YfJWM2ayv3XxQqPmhp147S3B1asoBkWvbyA/fupZXHrVloqVQL696elWTP6O2KMsQKSyfLe5VBZfVilcfr06Th58iSWL1+OmjVrQldXFwMGDEBybkW0/qP5wflWJpMhPT290ONljBWOxETqxrh4MXU+k8kyS+obGkod3adxkqas1NSo0p+ZGVC7NndnY5nU1IA2bWjx8AACAqiFbf9+6mC9cSMtABV/adgQaNAg86e1Nf89McZUjpaWFtLS0j65XUBAAEaMGIG+ffsCAOLi4vDo0aMijo4xVlyEAA4dAqZOzSz2bW8PrFkD2NpKG1t+cJKmzJS1kyxTHurqwBdf0PLLL4C/PyVsJ08Cjx/TeMZnz2jy7AyamtQR+8PkzcxMNcb1McZKJQsLC1y+fBmPHj1C2bJlc23lqlmzJnx8fODo6AiZTIbZs2dzixhjKiI4mErq+/rSfVNTGjEyZEjJ+4rDSRpjqkJDA+jUiRYAiIkBbt+m8v43b2b+jI2l27duKT6/QgXFpK1hQ+peyxNrM8ZKgGnTpmH48OGoW7cuEhISsG3bthy3W7VqFUaNGoVWrVqhcuXK+OGHHxCb29hvxliJ8PYtjTNbu5ZqzmlpAdOmATNnltyvMTxPWhH63Hl6zp+nYWdGRkUQHCudhKCpGz5M3EJD6aiWkxo1sidvNWsqT6VQxnLA86TlLj/zpDHlxL8nxkhaGrBlC+DmRsXAAaB3bxq+b2UlbWw54XnSVEB6OtCjBzV6WFhQX9qMacAaN+YhRayAZDLA3JyWL7/MXJ+URHPsfZi8RUZSh+6wMOrgnUFHh4rXfJi8lYSRuIwxxhgrGU6cAH79FViwgL4AZ3HuHJXUDwqi+zY2NFTfwaHYoywSnKQpqZcvaY7pO3doPuNHj4A9e+gxbW0a+JiRtLVsCVSrJmW0rMTT1qaD3wcHQERHZ3aNzEjebt+mGW4DA2nJKmMi86zJW926NJE6Y4wxxlheXblCUwwlJgL//EPZWKVKePYMmDEj83uxgQF1dRw/XrWKnnOSpqSMjOj7cGws/Y1eupS5vHpFFdgvXMjc3swsM2Gzt6e5kPl7MftslSvT3GsdOmSuS0+nlrWsLW63bgEPHgBRUcBff9GSQU2NWt3at6elXTtucWOMMcZY7p48AXr1ogRNJgOePkXaUCcsaX0Ei5eoIT6eVo8eDSxcqJpfK3hMWhEqijERQtB34YyE7eJF+o784XAiDQ1qFMlI2lq2BCwtS15lG1aCvH9PZZWyJm83b9JVhQ/VrZuZtLVvDxgbF3+8TKXxmLTc8Zi0ko9/T0ylvXsHtG5N3yUaNoRY+yvSO3eBenIifsQiuONHtG5NRa2bNpU62PzJz7mJk7QiVFxfEt6/p15nGUnbxYvAv/9m387QULGLZLNmJbfiDSshhKBxbRcu0PQA/v7Zq0oCNHdb1qTNzKz4Y2UqhZO03HGSVvLx74mprNRUqvxx7BhgZISn3lcwam51VP9rC7ZgNNKghjM//oVOCzuUyIYHTtKUhFRfEjIK+GUkbZcuAdevAykpitupqdGQoayJW+3atJ6xIhMdTRNwZyRt//xDf7RZWVnR3G8ZSVv16pKEykouTtJyx0layce/J6ayJk+mJjIdHbw95A+7cc3x8CGgrSVwoc5INL25g8YEBQUBJiZSR5tvnKQpCWX6kpCYCNy4kZm0XbpEidyHKlQAWrTI7CbZvDlQvnxxR8tKlTdvFJO2oCAa95aVhQUlaxmJm4UF991lH6VMx19lw0layce/J6aSPD2p+geA5N1/oKPnAJw/TwWpT58GrEzi6Uvq7dv0XeCvv2h8TwmSn3OT5G0mnp6e8oOMra0tAgICPrp9UlIS3NzcYG5uDm1tbVhZWWHr1q3yx1NSUjB//nxYWVlBR0cHjRo1wokTJxT24e7ujmbNmkFfXx+Ghobo06cPQkNDFbYZMWIEZDKZwtKyZcvCe+PFTEeHEq8pUwAvL+DxYyAiAvD2BqZPB9q2pUIjb95QtdO5c4GuXSlpq1sXGDUK2LSJWuSSkqR+N0ylVKhAg4NXrACuXQNevwaOHqU/zObNaT62R4+AHTuAkSNp3jZzc8DJCdi8mQZp8rUmxhhjrOQ6eZLq6QMQixZj5FFK0AwMqOejlRWAMmWAP/6gsTr+/sBPP0kbcxGTtCXNy8sLTk5O8PT0ROvWrbFhwwZs3rwZwcHBqJ5L96bevXvj33//xcKFC1GzZk1ERUUhNTUVrVq1AgD88MMP2LVrFzZt2oQ6derg5MmTcHFxwYULF9CkSRMAQLdu3TB48GA0a9YMqampcHNzw61btxAcHAw9PT0AlKT9+++/2LZtm/y1tbS0ULFixTy/v5J2JTclheo8ZC1K8vBh9u00NSlxa9KEBmw2aQI0agTo6xd/zKwUePcuc0ybnx9w9Sr1Wc/K1FRxTJu1Nbe0lXIl7fhbnLglreTj3xNTKXfuAK1aUUnz4cMxp/o2zF8gg4YGcPw40LnzB9vv3Qt8/TXdPnIE6Nmz2EMuqBLT3bFFixZo2rQp1q1bJ19nY2ODPn36wN3dPdv2J06cwODBgxEWFpZrsmRqago3NzeM/6+5FAD69OmDsmXLYteuXTk+5+XLlzA0NIS/vz/atWsHgJK0t2/f4uDBg3l+P0lJSUjK0swUGxuLatWqlegvCS9fApcvZyZuQUHU0PEhmQyoWZMStqzJW5UqxR8zU3Hv39MVhIzukZcvA8nJitsYGSkmbXXrctJWynCSljtO0nJmYWEBZ2dnODs7AwBkMhkOHDiAPn365Lj9o0ePYGlpiaCgIDT+cI7JIlaaf09MxURFURfGR4+Adu2wa7gvnL7VAkA9uEaPzuV548dT98gKFejLqbl5sYX8OfJzbpKsI2dycjICAwPh6uqqsN7BwQEXsk4AlsXhw4dhZ2eHZcuWYefOndDT00OvXr2wYMEC6P43KVhSUlK2A5auri7OnTuXaywxMTEAkC3x8/Pzg6GhIcqXL4/27dtj0aJFMPzIRAzu7u6YN29e7m+6BKpSBfjyS1qAzKIkQUGKy7NnwP37tOzbl/l8M7PMxC1jMTfn78vsM+jp0WW1jEtrCQl0BSEjacsob7pvX+YfY+XKND/bF1/QnG/160sWPmOsZIiMjESFChUKdZ8FuQDMmMpKTAT69KEErWZNnHPxwaivKEFzdf1IggYAK1fSRMLXrgEDB9LYdi2t4oi62EiWpEVHRyMtLQ1GRkYK642MjPDixYscnxMWFoZz585BR0cHBw4cQHR0NMaNG4fXr1/Lx6V17doVK1euRLt27WBlZYXTp0/j0KFDSPtwIrH/CCHg4uKCNm3aoH6WL27du3fHV199BXNzc4SHh2P27Nno2LEjAgMDoa2tneO+Zs6cCRcXF/n9jJY0VSKTUZJlbk7/VxlevsyeuN2/T+PeIiKoNTpDhQrZEzdraxp6xFi+6eoqTridmEgH7oyk7cIFqijp40MLACxYAMyaJV3MjDGlZ8zzNzJWdISgggcXLwLlyyPslyPoNbQSUlKAr74CFi36xPO1telCbNOmdM6fNo2qQqoSIZGIiAgBQFy4cEFh/cKFC4W1tXWOz+nSpYvQ0dERb9++la/z9vYWMplMxMfHCyGEiIqKEr179xZqampCXV1d1K5dW4wbN07o6urmuM9x48YJc3Nz8fTp04/G+/z5c6GpqSm8vb3z/B5jYmIEABETE5Pn56iS2FghAgKE+OUXIUaOFKJxYyE0NYWg/0zFRVdXiBYthBg7VoiNG4W4elWIhASp3wFTCUlJQpw/L8SiRUJ07kx/cGpqQly+LHVkrAiV9uPvx3zss0lISBDBwcEiIeMAnJ4uRFycNEt6ep7f0/r164WpqalIS0tTWO/o6CiGDRsmHjx4IHr16iUMDQ2Fnp6esLOzE76+vgrbmpubi1WrVsnvAxAHDhyQ3798+bJo3Lix0NbWFra2tsLHx0cAEEFBQUIIIVJTU8WoUaOEhYWF0NHREbVr1xYeHh7y58+ZM0cAUFjOnDkjhBDi2bNnYuDAgaJ8+fKiYsWKolevXiI8PDzX95vt98RYSTNnDp2PNTTE2wN/CysrutuihRD/faXPm8OHM79M7ttXVNEWmvycmyRrSatcuTLU1dWztZpFRUVla13LYGJiAjMzMxgYGMjX2djYQAiBZ8+eoVatWqhSpQoOHjyIxMREvHr1CqampnB1dYWlpWW2/U2cOBGHDx/G2bNnUbVq1Y/Ga2JiAnNzc9y/f78A77Z00tcH2rShJUNSEo0Pzdri9s8/NMzo8mVaMmhoADY2iuPcGjcGeHgJyxctLRqQ3KoV8OOPwNChwO+/A8OH0x8gj+dgLHfx8VRJTQpxcdS9OQ+++uorTJo0CWfOnEGnTp0AAG/evMHJkyfx559/Ii4uDj169MDChQuho6ODHTt2wNHREaGhobkWKsvq/fv3+PLLL9GxY0fs2rUL4eHhmDx5ssI26enpqFq1Kvbt24fKlSvjwoUL+P7772FiYoKBAwdi2rRpCAkJQWxsrLwoWcWKFREfH48OHTqgbdu2OHv2LDQ0NLBw4UJ069YNN2/ehJaKdeFiDLt3A/8ND0pZsx49l3fAw4c0u87hw9RBJs8cHYEZM4Bly4Bvv6VKdrVrF0nYxa7oc8bcNW/eXIwdO1ZhnY2NjXB1dc1x+w0bNghdXV3x7t07+bqDBw8KNTU1eUvah5KTk4WVlZWYOXOmfF16eroYP368MDU1Fffu3ctTrNHR0UJbW1vs2LEjT9sLwVdy8yo1VYi7d4X4/Xchpk+nxo5KlXJucQOEsLISYsAAIdzdhfDzowuujOXZq1dCGBvTH9O0aVJHw4oIH39zl6+WtLi43A/GRb3k8+Deq1cvMWrUKPn9DRs2CGNjY5Gamprj9nXr1hVr1qyR3/9YS9qGDRtExYoVxfv37+WPr1u3TqElLSfjxo0T/fv3l98fPny46N27t8I2W7ZsEdbW1iI9S8thUlKS0NXVFSdPnsxxv9ySxkqsc+eE0NISAhDp02eIwYPp393AQIjg4ALuMyVFiLZtaUcNGgiR5f9U2ZSIljQAcHFxgZOTE+zs7GBvb4+NGzfiyZMnGDNmDAAa4xUREYHffvsNADBkyBAsWLAAI0eOxLx58xAdHY3p06dj1KhR8sIhly9fRkREBBo3boyIiAjMnTsX6enpmDFjhvx1x48fj99//x2HDh2Cvr6+vDXPwMAAurq6iIuLw9y5c9G/f3+YmJjg0aNH+PHHH1G5cmX07du3mD8l1aeuTmPSrK0zK6oKQcVIrl9XbHV7+pSmBXj4ENi/P/P5jRvT5NutWtFPLk7CclWxIrBxY+bcbH36AK1bSx0VY8qpTBlq0ZLqtfNh6NCh+P777+Hp6QltbW3s3r0bgwcPhrq6Ot6/f4958+bhyJEjeP78OVJTU5GQkIAnT57kad8hISFo1KgRymSJyd7ePtt269evx+bNm/H48WMkJCQgOTn5k5UfAwMD8eDBA+h/MI9NYmIiHuY0Dw5jJVVYGJ1zk5OBvn0xR8sde/dSzylvb+o9VSAaGlSWv0kT4NYtYMIEIMscyiWVpEnaoEGD8OrVK8yfPx+RkZGoX78+jh07BvP/ymhGRkYqHEDLli0LX19fTJw4EXZ2dqhUqRIGDhyIhQsXyrdJTEzErFmzEBYWhrJly6JHjx7YuXMnypcvL98mo+T/F198oRDPtm3bMGLECKirq+PWrVv47bff8PbtW5iYmKBDhw7w8vLKdhBlRUMmA6pVo6V378z10dGZCdvVq1QT4vlzIDCQlrVraTsTE8WkrWlT7tXGsnB0pO6OO3YAI0ZQn9t8fiFkrFSQyfLc5VBqjo6OSE9Px9GjR9GsWTMEBARg5cqVAIDp06fj5MmTWL58OWrWrAldXV0MGDAAyR9O35ELkYfZivbt24cpU6ZgxYoVsLe3h76+Pn7++WdcztqPPwfp6emwtbXF7t27sz1WheexYari7VsqFR4dDTRtit3ddmLB/9QAABs2AP/1Ui44U1MaytClC7BtG9C2LTBy5GeHLSVJkzQAGDduHMaNG5fjY9u3b8+2rk6dOvD19c11f+3bt0dwcPBHX/NTB1tdXV2cPHnyo9swaVSuTP9/XbrQfSGode3CBSoQdOECcOMGEBmpWMxPS4sStYyhSfb29P/MSjEPD+Cvv4AHD4CZM4HVq6WOiDH2GXR1ddGvXz/s3r0bDx48QO3atWFrawsACAgIwIgRI+S9YeLi4vDo0aM877tu3brYuXMnEhIS5D13Ll26pLBNQEAAWrVqpfCd5sOWMC0trWzVpps2bQovLy8YGhrynH5MNaWkUJn8kBDAzAznXf/EyKF08WfmTCryWCg6daKxbj/9RPOo2dkBDRoU0s6Ln5rUATD2OWQyoHp1YPBg+o599SoQE0OV15csoVa4KlWoZf3SJZpWY8AAmr/NwoK6V65ZQ9NspKRI/W5YsSpfHtiyhW7/8gv90TDGSrShQ4fi6NGj2Lp1K7755hv5+po1a8LHxwc3btzAP//8gyFDhiA9PT3P+x0yZAjU1NTw7bffIjg4GMeOHcPy5csVtqlZsyauXbuGkydP4t69e5g9ezauXr2qsI2FhQVu3ryJ0NBQREdHIyUlBUOHDkXlypXRu3dvBAQEIDw8HP7+/pg8eTKePXv2eR8IY1ITApg0CfD1BcqUQfgvf+LL703leVuWznCFw80N6NqV5lAdMACIjS3kFyg+nKQxlVOmDM1b/MMPwMGDNK/xgwfAb78BY8dS4R81NeDxY+rCPGkS0KwZYGAAtG9PV3UOH6a535iK69oV+O47uj1ypHRjb5jK8vT0hKWlJXR0dGBra4uAgIBct/Xz84NMJsu23L17V76Nj48P7OzsUL58eejp6aFx48bYuXNncbyVEqFjx46oWLEiQkNDMWTIEPn6VatWoUKFCmjVqhUcHR3RtWtXNG3aNM/7LVu2LP78808EBwejSZMmcHNzw9KlSxW2GTNmDPr164dBgwahRYsWePXqVbaeQt999x2sra1hZ2eHKlWq4Pz58yhTpgzOnj2L6tWro1+/frCxscGoUaOQkJDALWus5Fu9Gli/HpDJELN+DzpPb4K3b4GWLYHt2+n7WKFSUwN27QKqVgXu3aNzfB66KysjmchLR2tWILGxsTAwMEBMTAwfaJXMu3c092FGF8lLl4A3b7JvV7OmYhfJevV40m2V8+4ddYd4/BgYMwb4b8wqK9mU4fjr5eUFJycneHp6onXr1tiwYQM2b96M4ODgHMu++/n5oUOHDggNDVWIuUqVKlD/78Dj5+eHN2/eoE6dOtDS0sKRI0cwdepUHD16FF27ds1TXB/7bBITExEeHi5PLJly4t8TKxH+/JO6NAmBFPfl+OLPqbhwAbC0pO9dhoZF+NoXL9IV+9RU6jI1YUIRvlje5efcxElaEVKGLwksb9LTgdDQzKTt4kUgp6GN+vpAixaZSVvLltRrjpVwf/+dOWr51KnMQY+sxFKG42+LFi3QtGlTebEqgOb27NOnD9zd3bNtn5GkvXnzRqHY1ac0bdoUPXv2xIIFC3J8PCkpCUlJSfL7sbGxqFatGidpJRj/npjSu3GDJsp9/x7iu+/xdcx6eO2ToXx5+p5V4EqO+bFqFeDiAmhqAufOAc2bF8OLflx+zk3c3ZExUOu4jQ0NXt28mSbcfv0aOH4cmD0b6NyZ5nN9945qTcyfD3TvDlSoQK1r331HlSV9fYEnTyjpYyVIx440yBigyTBjYqSNh5V4ycnJCAwMhIODg8J6BwcHXLhw4aPPbdKkCUxMTNCpUyecOXMm1+2EEDh9+jRCQ0PRrl27XLdzd3eHgYGBfKlWrVr+3gxjjOVHZCRVUX7/HujcGXMqrYXXPtnnl9rPL2dnoF+/zMIlr18X0wsXDsmrOzKmrCpUALp1owUA0tIoectaSfLBA2px+7DVTVcXqFUrc/632rUzbxsYFP97YXmwdCll5WFhdOUto6gIYwUQHR2NtLQ0GBkZKaw3MjKSz835IRMTE2zcuBG2trZISkrCzp070alTJ/j5+SkkYTExMTAzM0NSUhLU1dXh6emJLh9p/Z05cyZcXFzk9zNa0hhjrNDFx9M8pM+eAXXqYHefP7BggiYAmqK0Y8dijEUmo/nS/vmHJtgdNoyKDhT6QLiiwUkaY3mkrg40bEjLf/Ot4+VLStguXaJELTSUjgMJCcDNm7R8yNAwM2HLmsDVqEEt8kwieno0irl9ezqo9+8P9OghdVSshJPJZAr3hRDZ1mWwtraGtbW1/L69vT2ePn2K5cuXKyRp+vr6uHHjBuLi4nD69Gm4uLigRo0a2eb+zKCtrQ1tbe3PfzOMMfYx6emAkxOVzK5UCRfdjmDEyPIAqOiiJNOWGRgA+/fT+JSjR4FlywBXVwkCyT9O0hj7DFWq0AWjXr0y16WmAo8eUcIWGkrFhTJuR0YCUVG0fFjkTV2dErWcEjgjI7ogxIpY27bUPWLVKurDevs2Nakylk+VK1eGurp6tlazqKiobK1rH9OyZUvs2rVLYZ2amhpq1qwJAGjcuDFCQkLg7u6ea5JWEDxcXbnlZ/oAxoqNmxtNUKulhcerD6LHBCukpgKDBtEwEck0bkzFQ77/nmK0t6cLskqOkzTGCpmGBlWFrFkT6NlT8bHYWOD+/cykLSOJu3ePum7fv0/LkSOKzytXTrHLZEYCV7s2TTnACtGiRXS17d49YPJkmruBsXzS0tKCra0tfH195RMoA4Cvry969+6d5/0EBQXBxMTko9sIIRQKg3wOTU1NyGQyvHz5ElWqVMm11Y9JQwiB5ORkvHz5EmpqatDS0pI6JMbItm00QS2A2FVb0GF2G7x9S4XWiqTUfn6NHk1Xx3fupMl1g4IAY2OJg/o4TtIYK0blygG2trRkJQQQEaHY6paRwD16RMndtWu0fKhaNcUErnZtoH59miKEFYCuLrBjB9C6NR3M+/enEsKM5ZOLiwucnJxgZ2cHe3t7bNy4EU+ePMGY//pLz5w5ExEREfjtvwsBHh4esLCwQL169ZCcnIxdu3bB29sb3t7e8n26u7vDzs4OVlZWSE5OxrFjx/Dbb78pVJD8HOrq6qhatSqePXuGR48eFco+WeErU6YMqlevDjXJv/kyBsDfH/jf/wAAKT/MQrdd3yA8nHoHHTwIKEUBUpmMpti5fp0KDAwZQtXelHheJU7SGFMCMhklVVWrZh9Um5hI49w+7DoZGkqFip4+peX0acXndetGE3q3b89dJfOtZUtg+nQqJvL995SwVa4sdVSshBk0aBBevXqF+fPnIzIyEvXr18exY8dgbm4OAIiMjMSTJ0/k2ycnJ2PatGmIiIiArq4u6tWrh6NHj6JHlrGR79+/x7hx4/Ds2TPo6uqiTp062LVrFwYNGlRocZctWxa1atVCSkpKoe2TFR51dXVoaGhwKydTDvfvyysoioGD4BQ2Dxcv0vRER4/SsBCloacH/PEH0KwZcOYMMGcOsHCh1FHliudJK0LKME8PU22vXuU89u3u3cxpAFq0oDGyvXopQXeDkiQpiZo879yhDvV790odEcsHPv7mjj8bxliheP2aLmrevw+0bIk5bf/G/J91oalJU44W4jDZwrVnD7WkAcCxYzSnUjHhedIYKyUqVaL+3iNHAu7uNF73zh1K2MaMAbS1gcuXgb59aT63bduA5GSpoy4htLWp26O6OuDlRVffGGOMMUZfJvr1owTN3Bx7Bh3E/J91AQCbNilxggYAX38NjB1Lt7/5hia4VUKcpDGmgqysqOv148fAzJlUgfbuXZqsu0YNYOVKmpibfYKtLfDjj3R77Fjg33+ljYcxxhiTmhB0JdjfH9DXx+XZRzBsOlWtnTULGD5c4vjyYtUqOse/fk0TXSvhFWxO0hhTYUZGwOLFdJFo2TLAxIQKlEydCpibA7Nn01xv7CNmzQIaNaK+pWPH0smJMcYYK62WLaOuOWpqeLJ8H7pOrY/UVGqgkrTUfn5oa1MPmfLlqcvRjBlSR5QNJ2mMlQLlylEdjPBw6oZQqxbw5g2NlzU3ByZOpCqSLAdaWtTtUVMTOHAA+P13qSNijDHGpOHjI58M+t3C1Wjv3g0xMVRfa+vWElaozNKSzu8AsHo1TXqtRDhJY6wU0damqUJCQuhYZGsLJCQAa9fSvG7ffAPcvCl1lEqoUSPgp5/o9sSJwPPn0sbDGGOMFbdr1+iLAoDUMRPgcHgCHj2iIRYHDihJqf386tWLrmIDNCbk/n1p48mCkzTGSiF1dZr+6+pV4K+/gM6dgbQ0YPduykd69qQ5H7lnXxY//EBZ7Zs3VJafPxzGGGOlxbNnlNAkJEB0645vXq7CpUtAhQpKWGo/vxYtAtq0ocH6AwbQ1WslwEkaY6WYTAZ06kTzOV67Bnz1Fa07dgxo146OWYcPZ5bzL9U0NalbhJYWnZG2b5c6IsYYY6zoxcUBjo5AZCRQvz7m2eyFl7eGfBSAtbXUAX4mTU2aZqdKFepONHGi1BEB4CSNMfYfW1tg3z6aZ+377ykXuXAB6N0baNCA8pNSP7dtvXrAggV029mZZhFnjDHGVFVaGs0pduMGYGQEL6cjmLeK5vfavBlo317a8AqNmRmNOZfJgC1bMseqSYiTNMaYglq1gA0bqJDIDz9Q0ZHgYGDECOp3vno18P691FFKaOpUmrwzNpYG+HG3R8YYY6pq+nTgzz8BHR1cnXUI37iZA6Bh2sOGSRxbYevcGZg7l26PHQvcuiVpOJykMcZyZGICLFlC5fuXLKFy/k+fUgNS9ep0HIuOljpKCairU1dHHR3g1Ckql8kYY4ypmg0baD4xAM8W7UCXWS2QmkoNaxm5jMqZNQtwcKBxaV99JemkspInaZ6enrC0tISOjg5sbW0REBDw0e2TkpLg5uYGc3NzaGtrw8rKClu3bpU/npKSgvnz58PKygo6Ojpo1KgRTpw4ke/XFUJg7ty5MDU1ha6uLr744gvcuXOncN40YyWIgQG1qD16BKxfT61pr18D8+ZR+f7Jk2nS7FLF2pomoAOoZY3nL2CMMVYA6elUVFBXF2jYkOYaW7CAKt2HhgKpqRIF5usLjB8PAIj7YQHarhmImBgaq75lSwkrtZ8famrArl3U/TE0FPjuO+l6zAgJ7d27V2hqaopNmzaJ4OBgMXnyZKGnpyceP36c63N69eolWrRoIXx9fUV4eLi4fPmyOH/+vPzxGTNmCFNTU3H06FHx8OFD4enpKXR0dMT169fz9bpLliwR+vr6wtvbW9y6dUsMGjRImJiYiNjY2Dy/v5iYGAFAxMTE5POTYUx5paYK4eUlRJMmQtCRSwgNDSGcnIS4dUvq6IpRWpoQbdvSB/DFF3SfKQ0+/uaOPxvGlEN6uhDOzpnn0pwWbW0hGjYU4uuvhVi4UIgDB4S4d4/OxUUmOFgIAwMhAJEyxEm0aJ4uACFq1hTi5csifF1lcv48fbkBhFi7ttB2m5/jr6RJWvPmzcWYMWMU1tWpU0e4urrmuP3x48eFgYGBePXqVa77NDExEWs/+DB79+4thg4dmufXTU9PF8bGxmLJkiXyxxMTE4WBgYFYv3593t6c4BMhU23p6UKcOiVEx46KJxRHRyHOnZM6umLy4IEQZcrQG1+zRupoWBZ8/M0dfzaMKYdlyzLPnevXC3HkiBBLlwoxbJgQtraZp5fckrdGjYQYMkSIRYuEOHhQiPv3CyF5i4sTokYNIQCR3qaNGNw3UQBCVKwoRGhoYbzrEmT5cvqwNTWFuHKlUHaZn+OvZN0dk5OTERgYCAcHB4X1Dg4OuHDhQo7POXz4MOzs7LBs2TKYmZmhdu3amDZtGhKyzGeQlJQEnQ9m09PV1cW5c+fy/Lrh4eF48eKFwjba2tpo3759rrFlvHZsbKzCwpiqksmALl2A06eBK1do3jWZjMYXt2kDtG1LlepVuq6GlRWwbBnd/uEH4MEDaeNhjDFWIuzcCcyYQbdXrgT+9z+ao3TGDCoseO0aDYd6+JCmwlmyBHByApo2pa6RSUnAP/9QQUI3N6BPHyr8VbYs0KQJzTnt7k7PffgwH1PpHD4MhIUBVatiQZMD2HtAW15qv3btovo0lJSLC32wKSk0Pu3162J9eY1ifbUsoqOjkZaWBiMjI4X1RkZGePHiRY7PCQsLw7lz56Cjo4MDBw4gOjoa48aNw+vXr+Xj0rp27YqVK1eiXbt2sLKywunTp3Ho0CGkpaXl+XUzfua0zeOPDL5xd3fHvHnz8vEpMKYamjUD9u+n7tvLl9MJ5tw54Msvgfr1gfnzgb59pY6yiIwdS4MH/v6bSmD6+1NxEcYYYywHJ0/SODQAmDYNmDIl5+3U1IAaNWhxdMxcn5ZGQ6Hv3FFc7t4FEhOpWv6NG4r70tUFbGxoJpl69YC6demnhQW9jpy3NwAgqOFwzFlTGQCwdSvNnVrqyGTAtm00d1pYGDB8OHDo0AcfWNGRvHCI7IORh0KIbOsypKenQyaTYffu3WjevDl69OiBlStXYvv27fLWtNWrV6NWrVqoU6cOtLS0MGHCBIwcORLqH3xpysvr5ic2AJg5cyZiYmLky1OeQ4mVMtbWVOwwPJxOPGXLArdvA/36Aa6uKjoptpoancHKlgXOn6c5ChhjjLEcXL1KPU9SU4GhQ4GlS/O/D3V16sjRqxcwcybVuQgKojmn798HDh4EFi2iKoyNGgHa2lSs8Pp1asFzdaXnWlkB+vqAnR3lHysXxiP1yHEAwPcn+gEA5syhVrlSq3x54I8/6EM8cgT4+edie2nJkrTKlStDXV09W6tZVFRUthasDCYmJjAzM4OBgYF8nY2NDYQQePbsGQCgSpUqOHjwIN6/f4/Hjx/j7t27KFu2LCwtLfP8usbGxgCQr9gA6hJZrlw5hYWx0sjMjI5jT55Q8UOATkT9+tFJROWYm1N/FQD48Ue6nMkYY4xlcf8+dWl8/56GC2zdWriNMurqQM2aQO/edCravZta1OLiqKeLjw+wcCFVkGzYENDSAuLjgcBA4LffgIDZJ6GRFI9wWOBaehN88w0laaVe06aZF2Dd3ICzZ4vlZSVL0rS0tGBrawtfX1+F9b6+vmjVqlWOz2ndujWeP3+OuCzf8u7duwc1NTVUrVpVYVsdHR2YmZkhNTUV3t7e6N27d55f19LSEsbGxgrbJCcnw9/fP9fYGGPZVahA3R937qSLUIcO0Xi1J0+kjqwIjB4NdO1KAwWGD5ewbjJjjDFl8+IFnSJevgRsbalXoZZW8by2hgaNJ+vbl3KM33+n8Wzv39M1RW9vGpbgYu4DADio1g8ODjJs3qzCpfbz6/vvqekzLQ0YPBj499+if81CKVVSQBml8Lds2SKCg4OFs7Oz0NPTE48ePRJCCOHq6iqcnJzk2797905UrVpVDBgwQNy5c0f4+/uLWrVqidGjR8u3uXTpkvD29hYPHz4UZ8+eFR07dhSWlpbizZs3eX5dIagEv4GBgfDx8RG3bt0SX3/9NZfgZ+wzXLgghKEhFUoyMqL7KufpU3nZYuHuLnU0pRoff3PHnw1jxSs2NnPaGisrIV68kDqiHCQlyc9f6QGlpURzPr17J4SNjRB16hS41GWJKcEvhBC//vqrMDc3F1paWqJp06bC399f/tjw4cNF+/btFbYPCQkRnTt3Frq6uqJq1arCxcVFxMfHyx/38/MTNjY2QltbW1SqVEk4OTmJiIiIfL2uEFSGf86cOcLY2Fhoa2uLdu3aiVv5nASKT4SMKXr0iOZ7ySgfvGuX1BEVge3b6Q1qaZWyieOUCx9/c8efDWPFJylJiM6d6bRgaEgztyil48cpSGNjnvfzY8LCKFkroPwcf2VCqHSBbEnFxsbCwMAAMTExPD6Nsf/ExVGPgcOH6b6bG3WzKKZiSUVPCBoQ8Oef1I/90iVAU1PqqEodPv7mjj8bxopHejoV3dizB9DTo+K/trZSR5WL77+nyl9jxwKenlJHo7Lyc/xVla9FjLESomxZmm/lhx/o/qJFNP3I+/fSxlVoZDJgwwYakHf9Ok1UwxhjrNSZPp0SNA0NKtqhtAlaWhqVhASowhdTCpykMcaKnZoaTcy5fTs1Mvn40OTX/xVpLflMTIBff6XbCxZQbWTGGGOlxooVmUV/t28HHBwkDefjzp2jiiYVKwLt20sdDfsPJ2mMMckMH05zQFeuTHlMs2bAlStSR1VIBg/OnAxn+HAgOVnqiBhjjBWD3btprlCAKhwPHSptPJ/kQ1Ud0asXd89XIpykMcYk1aYNTe5Zvz6VKG7fHvDykjqqQiCTUb/+ypWBW7do4B1jjDGVduoUMGIE3XZxyZwrVGmlp2cmaf37SxsLU8BJGmNMchYWwPnzNMlnYiI1Qs2dSzU4SjRDQ2D9erq9ZAllo4wxxlTStWs0pCs1lSaM/vlnqSPKg2vXaKxB2bJA585SR8Oy4CSNMaYUypWjya4zrjrOm0fJWny8tHF9tv796Y2kpVG3x8REqSNijDFWyB48AHr0oCJYnTvTOLQSUbU4oxWtZ09AR0faWJiCkvDnwxgrJdTVqf/+li3ULX7fPur++Py51JF9prVrAWNjICQE+OknqaNhjDFWiP79F+jWjWpvNGkCeHsDWlpSR5UHQlCwAHd1VEKcpDHGlM6oUYCvL1CpEvXEaNYMCAyUOqrPUKkSleUHKAu9cEHaeBhjjBWKd++oEerhQ6BGDeD4ceoZUiLcvk1NgNraQPfuUkfDPsBJGmNMKbVvT5Ue69allrS2bYH9+6WO6jP06gUMG0ZXLkeMUIF+nIwxVrolJ1MDVGAgUKUKcOIEYGQkdVT5kNHVsWtXGpPGlAonaYwxpVWjBjU6desGJCTQpNcLF5bggiKrVwNmZsD9+4Cbm9TRMMYYK6D09MxeH3p6wNGjQK1aUkeVT9zVUalxksYYU2oGBsCffwLOznR/9myacyYhQdKwCqZ8eWDzZrq9ejVw9qyk4bDsLCwsMH/+fDx58kTqUBhjSuyHH2g+NA0N6uXRrJnUEeXT/fs0PYyGBvDll1JHw3LASRpjTOlpaACrVlE1ew0NYM8eoEMHmletxOnWDRg9OrPbY1yc1BGxLKZOnYpDhw6hRo0a6NKlC/bu3YukpCSpw2KMKZGVK2l4MQBs3UqH9RLnwAH62aEDULGitLGwHHGSxhgrMf73P5ootEIF4PJloHlz4MYNqaMqgBUrgOrVgfBwuhzLlMbEiRMRGBiIwMBA1K1bF5MmTYKJiQkmTJiA69evSx0eY0xiv/+eOVXMsmWAk5O08RQYd3VUepykMcZKlA4dKEGztgaePgVatwYOHpQ6qnwqV44uvwKApycQFCRtPCybRo0aYfXq1YiIiMCcOXOwefNmNGvWDI0aNcLWrVshSuzASMZYQfn6UgcIgLrgT5smZTSf4elTqswlkwG9e0sdDcsFJ2mMsRKnVi3g0iWgSxcqkti3L7BkSQkrKNKpE01yDWT2m2FKIyUlBfv27UOvXr0wdepU2NnZYfPmzRg4cCDc3NwwdOhQqUNkjBWj69eBfv2AlBRg0CDqECGTSR1VAWV0dWzdmubwZEqJkzTGWIlUvjxw7BgwYQLdnzkTGD4cKFHDh2bMoJ9eXsDjx9LGwgAA169fx8SJE2FiYoKJEyeiXr16uH37Ns6dO4eRI0fCzc0Nhw8fxoGMLzmf4OnpCUtLS+jo6MDW1hYBAQG5buvn5weZTJZtuXv3rnybTZs2oW3btqhQoQIqVKiAzp0748qVK5/9vhljuXv4kKYRi4sDOnYEduwA1EryN+iM0vvc1VGpleQ/McZYKaehAaxZA/z6K6CuDuzcSSfQqCipI8ujJk2oRS0tjSqjMMk1a9YM9+/fx7p16/Ds2TMsX74cderUUdimbt26GJzRCvoRXl5ecHZ2hpubG4KCgtC2bVt07979k5UjQ0NDERkZKV9qZanr7efnh6+//hpnzpzBxYsXUb16dTg4OCAiIqJgb5gx9lFRUVQYJCoKaNyYGqG0taWO6jNERQEZF4v69pU2FvZRMsEd64tMbGwsDAwMEBMTg3IlZvp5xkqmv/6iedTevqWaHH/+CTRsKHVUeXDqFE0kqqdH4wQqVJA6IpVQ0OPv48ePYW5uXigxtGjRAk2bNsW6devk62xsbNCnTx+4u7tn297Pzw8dOnTAmzdvUL58+Ty9RlpaGipUqIC1a9di2LBheXoOn5sYy5u4OBoHfe0aYGlJ83aW+N6BmzYB338P2NkBV69KHU2pk5/jL7ekMcZUQufONE6tVi3gyRPqav/nn1JHlQddugCNGgHv3wNZvswzaURFReHy5cvZ1l++fBnXrl3L836Sk5MRGBgIBwcHhfUODg64cOHCR5/bpEkTmJiYoFOnTjhz5sxHt42Pj0dKSgoqfqSEdlJSEmJjYxUWxtjHJSdTb8Br14DKlYETJ1QgQQMyuzr26ydtHOyTOEljjKkMa2tK1Dp2pCugvXtTTQ6l7i8gk2WWCPvlFyAxUdp4Srnx48fj6dOn2dZHRERg/Pjxed5PdHQ00tLSYGRkpLDeyMgIL3KZ4M/ExAQbN26Et7c3fHx8YG1tjU6dOuHsRyY9d3V1hZmZGTp37pzrNu7u7jAwMJAv1apVy/P7YKw0Sk8Hvv2WOjqUKQMcPQrUri11VIXg7Vvg9Gm6zUma0uMkjTGmUipWpCue//sfJWfTp9PJVqkLigwaBFSrBvz7Lw2sY5IJDg5G06ZNs61v0qQJgoOD870/2Qfl34QQ2dZlsLa2xnfffYemTZvC3t4enp6e6NmzJ5bnUv1z2bJl2LNnD3x8fKCjo5NrDDNnzkRMTIx8ySkJZYxlmjkT2LWLxjrv309zcqqEI0eoPGW9enRVkyk1TtIYYypHU5N6Dv7yC1Xg2raNehW+fCl1ZLnQ1ASmTKHbK1bQZVwmCW1tbfz777/Z1kdGRkJDQyPP+6lcuTLU1dWztZpFRUVla137mJYtW+L+/fvZ1i9fvhyLFy/GqVOn0PATgy+1tbVRrlw5hYUxljMPD5qkGgC2bKGqjiqDuzqWKJykMcZUkkwGTJxIZfrLlaNiVi1aAHfuSB1ZLkaPBgwMgNDQEjKYTjV16dJF3vKU4e3bt/jxxx/RpUuXPO9HS0sLtra28PX1VVjv6+uLVq1a5Xk/QUFBMDExUVj3888/Y8GCBThx4gTs7OzyvC/G2Mft3Zt5vWzJEprWRWW8f0/dTABO0koIyZO0/MwhA9AAaDc3N5ibm0NbWxtWVlbYunWrwjYeHh6wtraGrq4uqlWrhilTpiAxyzgPCwuLHOeiyTreYMSIEdkeb9myZeG+ecZYkevalcap1agBhIcD9vbArFk0GFypxqrp6wNjx9LtjMu4rNitWLECT58+hbm5OTp06IAOHTrA0tISL168wIoVK/K1LxcXF2zevBlbt25FSEgIpkyZgidPnmDMmDEAqBti1oqMHh4eOHjwIO7fv487d+5g5syZ8Pb2xoSMyQBBXRxnzZqFrVu3wsLCAi9evMCLFy8QFxdXOB8AY6XU6dNAxr/jpEmZ01iqjBMngIQEOhk2aiR1NCwvhIT27t0rNDU1xaZNm0RwcLCYPHmy0NPTE48fP871Ob169RItWrQQvr6+Ijw8XFy+fFmcP39e/viuXbuEtra22L17twgPDxcnT54UJiYmwtnZWb5NVFSUiIyMlC++vr4CgDhz5ox8m+HDh4tu3bopbPfq1at8vb+YmBgBQMTExOTreYyxwhcdLUT79kJQakZL1apCjB8vxKlTQiQlSR2hEOL5cyG0tCi4LMc1ln+fc/yNi4sTGzZsEOPGjRNTp04VO3bsEMnJyQWK49dffxXm5uZCS0tLNG3aVPj7+8sfGz58uGjfvr38/tKlS4WVlZXQ0dERFSpUEG3atBFHjx5V2J+5ubkAkG2ZM2dOnmPicxNjiq5fF0Jfnw69AwcKkZYmdURFYMgQeoPTpkkdSamWn+OvpPOk5XcOmRMnTmDw4MEICwvLtdzwhAkTEBISgtMZ1WsATJ06FVeuXMm1lc7Z2RlHjhzB/fv35QO6R4wYgbdv3+LgwYMFfn88Fw1jyiUlBfjjD5qM9Phx6v2RwcAA6NkT6NOHJi7V15coyNGjaSBEnz4UKCsQPv7mjj8bxjJl9LD491/giy+owalET1adk6QkwNAQiI2lyd7s7aWOqNQqEfOkFWQOmcOHD8POzg7Lli2DmZkZateujWnTpiEhIUG+TZs2bRAYGIgrV64AAMLCwnDs2DH07Nkz1zh27dqFUaNGZau45efnB0NDQ9SuXRvfffcdoqKiPvqeeC4axpSbpiYwZAglatHRVFb5u+/o3BUTA/z+OzBwIM2J06MHsHEjkEu19KIzdSr9PHSIxqcxSQQHB+PEiRM4fPiwwsIYUx0vX1KX+H//pR6ABw+qYIIGUF/O2FjA1JQGZ7MSIe+lqgpZQeaQCQsLw7lz56Cjo4MDBw4gOjoa48aNw+vXr+Xj0gYPHoyXL1+iTZs2EEIgNTUVY8eOhaura477PHjwIN6+fYsRI0YorO/evTu++uormJubIzw8HLNnz0bHjh0RGBgI7Vz+g93d3TFv3rx8fhKMMSno6FAi1qMHVYK8fJlO0AcPAvfvU0vb8ePAmDF0TuvTh5Yir1psYwM4OlLxkBUrKFNkxSYsLAx9+/bFrVu3IJPJkNHZJOMiXlpampThMcYKSVwc9Z64fx8wN6fjvYGB1FEVkYyqjn37UsljViJI/pvKzxwy6enpkMlk2L17N5o3b44ePXpg5cqV2L59u7w1zc/PD4sWLYKnpyeuX78OHx8fHDlyBAsWLMhxn1u2bEH37t1hamqqsH7QoEHo2bMn6tevD0dHRxw/fhz37t3D0aNHc30vPBcNYyWTujrQqhXV6wgNBYKDgcWLaW4cIajwiKsrUKcOLa6utK7IKuVnjFj/7Te6xMuKzeTJk2FpaYl///0XZcqUwZ07d3D27FnY2dnBz89P6vAYY4UgKQn46ivg6lWgUiXg5EnggyKqqiM1la4+AlzVsYSRLEkryBwyJiYmMDMzg0GWSx02NjYQQuDZs2cAgNmzZ8PJyQmjR49GgwYN0LdvXyxevBju7u5I/+Ab1ePHj/HXX39h9OjRn4zXxMQE5ubmOc5Xk4HnomGs5JPJqDFr5kxqXYuIoJa2rl2pu2RoKLB0KXXpNzOjSbOPHy/kybJbtwZatqSdrllTiDtmn3Lx4kXMnz8fVapUgZqaGtTU1NCmTRu4u7tj0qRJUofHGPtMjx8DbdvS2LMyZajbu0rP6xwQALx6Rdlou3ZSR8PyoUBJ2o4dOxRalGbMmIHy5cujVatWePz4cZ72UZA5ZFq3bo3nz58rlBq+d+8e1NTUULVqVQBAfHw81D5oylVXV4cQAh/WSNm2bRsMDQ1zHa+W1atXr/D06dNs89UwxlSbqSl1eTxxgsYv7N0LDB5MhUVevKDeiD160Di2QYNoXNvbt5/5ojIZMH063fb0pH45rFikpaWhbNmyAOhi4vPnzwEA5ubmCOUxgoyVaMeOAU2aUAtahQo09Fflh2h5e9PP3r0BDclGObGCKEj5yNq1a4vTp08LIYS4cOGC0NXVFRs2bBCOjo6ib9++ed5PRgn+LVu2iODgYOHs7Cz09PTEo0ePhBBCuLq6CicnJ/n27969E1WrVhUDBgwQd+7cEf7+/qJWrVpi9OjR8m3mzJkj9PX1xZ49e0RYWJg4deqUsLKyEgMHDlR47bS0NFG9enXxww8/ZIvr3bt3YurUqeLChQsiPDxcnDlzRtjb2wszMzMRGxub5/fHZY4ZU12JiUKcOCHE2LFCmJoqlvbX0BCiSxch1q4V4unTAr5AaqoQNWvSDj08CjX20qCgx982bdqIAwcOCCGE+Prrr0W3bt3EuXPnxLBhw0S9evWKINLix+cmVtqkpAjx44+Zx+hmzYQID5c6qmKQlpZ5gvpgOg8mjfwcfwuUpOnq6srnMpsxY4Y8kbp9+7aoXLlyvvaVnzlkhBAiJCREdO7cWejq6oqqVasKFxcXER8fL388JSVFzJ07Vz7XTLVq1cS4cePEmzdvFPZz8uRJAUCEhoZmiyk+Pl44ODiIKlWqCE1NTVG9enUxfPhw8eTJk3y9Nz4RMlY6pKUJcfmyEDNnClG3rmLCBghhZyfEggVC3LolRHp6Pna8fj3twNycvmWwPCvo8ffEiRPC29tbCCHEw4cPhY2NjZDJZKJy5cryi5MlHZ+bWGkSGSnEF19kHo/Hj6eLbKXCxYv0pvX1S9GbVm5FPk+aoaEhTp48iSZNmqBJkyaYMmUKhg0bhocPH6JRo0YK3RFLM56LhrHS6d496kZz8CBw8SJ9NchgZUVVInv3pmIl6uof2VFCApUde/mS+lF+/XURR646CvP4+/r1a1SoUCHXolYlDZ+bWGnh70/d01+8APT0gM2b6X6pMX06sHw5nTt+/13qaBiKYZ60Ll26YPTo0Rg9ejTu3bsnH9N1584dWFhYFGSXjDGmMmrXpnPj+fNAZCSwaROVetbWBh4+pMr67dpRNbEffgByrequqwtMnEi3ly1TzPZYoUtNTYWGhgZu376tsL5ixYoqk6AxVhqkp1OBp44dKUGrVw+4dq2UJWhCZJbe799f2lhYgRQoSfv1119hb2+Ply9fwtvbG5UqVQIABAYG4mu+0ssYY3JGRsDo0cCRIzSB9v79wDffAOXLUwPZsmWUxOVq3DgqQXbjBk1IyoqMhoYGzM3NeS40xkqwN2+op4KrKyVrTk5UqbdOHakjK2Y3bwJhYTQpaLduUkfDCqBA3R1Z3nCXEsZYblJSKEGbNQuoWJEmVK1YMZeNJ02iUvwODjShD/ukgh5/t23bhj/++AO7du1CxVx/ISUbn5uYqrp2jeY/e/SIei6sWUMXyUplQ/hPPwELFlD/+gMHpI6G/afIuzueOHEC586dk9//9ddf0bhxYwwZMgRv3rwpyC4ZY6xU0dSkro716gGvXwNz5nxk4ylTADU14NQp4J9/ii3G0uiXX35BQEAATE1NYW1tjaZNmyosjDHlIwTNZ9m6NSVoNWoAFy4A331XShM0gLs6qoACTZgwffp0LF26FABw69YtTJ06FS4uLvj777/h4uKCbdu2FWqQjDGmijQ0gNWrgc6d6QvG//4H1K+fw4aWlnR52MsL+PlnYNeuYo+1tOjTp4/UITBWKO7coZak7t2BXr1UN1mJiwO+/x7Ys4fu9+kDbNtGXcpLrdBQ+gPQ0AC+/FLqaFgBFai7Y9myZXH79m1YWFhg7ty5uH37Nvbv34/r16+jR48eePHiRVHEWuJwlxLGWF70708XPTt2BP76K5cvU4GBgJ0dlYMMCwOqVy/2OEsSPv7mjj8b1XfuHODoCLx9S/cbNqSu1f37U6O8qggOBgYMAEJC6NC4dCng4qK6CWmeubsDP/4IdO0KnDghdTQsiyLv7qilpYX4+HgAwF9//QUHBwcAVAErNja2ILtkjLFSa/lyGj/x998fGTpga0tZXFoa4OFRnOExxkqQw4eBLl0oQatXDyhblmpIDBwINGhAldhVoTbO7t1As2aUoJmaAn5+wNSpnKAB4K6OKqJASVqbNm3g4uKCBQsW4MqVK/IS/Pfu3UPVqlULNUDGGFN1lpZUsh+gLxkJCblsmLHRpk1UwowVOjU1Nairq+e6MKbMtm0D+vUDEhOpJe3qVeDxY6ohYWBALU9DhwI2NsD27VTAqKRJTATGjKEqufHxQKdOQFAQ0KaN1JEpicePqYKKmhqVuWQlVoGStLVr10JDQwP79+/HunXrYGZmBgA4fvw4unGZT8YYyzdXV6BqVRr0vmJFLht17UqXwuPigPXrizO8UuPAgQPw8fGRL15eXnB1dYWJiQk2btwodXiM5UgI6uo3ahS1ko0cSY0purpUNXbePPruvmBBZjXZkSMBa2u65pOcLPU7yJuwMCoOsmEDtZj99BMVvDU0lDoyJZLRHaNtW/5gSjguwV+EuN8/Yyw/9uwBhgyhadHu3gWqVctho507gWHDAGPjzDrTLJvCPv7+/vvv8PLywqFDhwohOmnxuUm1pKcD06YBq1bR/R9+oCFJuXX7e/eOChUtX05zNQJ0rPnhB+Dbb2laLWV0+DAd+mJigEqVqLtj165SR6WE2rUDAgKoKtWkSVJHwz6Qn+NvgZO0tLQ0HDx4ECEhIZDJZLCxsUHv3r25O0gWfCJkjOWHEHR+PXcOGDw4s1qZgpQUqi/97BmweTN9q2LZFPbx9+HDh2jYsCHev39fCNFJi89NqiM5mVrPdu+m+ytX0owdeREfD2zcSPM1RkbSOhMTYMYMqpZYpkzRxJxfqamAmxvFCQD29lToNseLWKXdixc0QE8I4MkT/pCUUJEXDnnw4AFsbGwwbNgw+Pj4YP/+/XByckK9evXw8OHDAgXNGGOlnUwG/PIL/dy7ly6GZqOpCTg70+3ly+kyOitSCQkJWLNmDY+5Zkrl/Xsqrb97N1Va37kz7wkaQEmYszN1IVy7lr7PR0bSPiwtKSl6967Iws+T58+pXlJGgubsTAVCOPfIxaFDlKA1b84fkgooUJI2adIkWFlZ4enTp7h+/TqCgoLw5MkTWFpaYhI3rTLGWIE1aQKMHk23J03KpQrbd98B5cpRn8gjR4o1PlVXoUIFVKxYUb5UqFAB+vr62Lp1K37++Wepw2MMAPDqFRXMOHmSkq3Dh6mQRkHo6ADjxwMPHlDLmqUlEBVF3R8tLICFC6mLYXE7fZqOhwEBgL4+sH8/denU0ir+WEqMjKqO/fpJGwcrFAXq7qinp4dLly6hQYMGCuv/+ecftG7dGnFxcYUWYEnGXUoYYwXx8iVQqxZ9MdqwgboeZePqSpUC2rTJpcmtdCvo8Xf79u2QZRnMo6amhipVqqBFixaoUKFCUYRa7PjcVLI9eUJjse7epSIgR48CLVsW3v5TUqhM/6JFVGAEoMqQkyZRS1bFioX3WjlJTwcWLwbmzKHbDRtSglarVtG+bon35g0VCklNBe7d4w9MSRX5mLSKFSviyJEjaNWqlcL68+fPw9HREa9fv87vLlUSnwgZYwW1ejV9Iapcmc632fKD58/pMndKCnDxYuF+S1MBfPzNHX82JVdwMCVoz55RNdhTp6icflFIS6OxX4sW0esCNOfahAk0YXSVKoX/mq9eAU5OwPHjdH/UKOqKqatb+K+lcn77DRg+nCoA37wpdTQsF0U+Ju3LL7/E999/j8uXL0MIASEELl26hDFjxqBXr14FCpoxxlimceOAunWB6Ggqn52NqWlm/ybuhldotm3bhj/++CPb+j/++AM7duyQICLGyMWL1HD+7BklZhcuFF2CBgDq6lRt9tYt4I8/qEUrLg5YsoSuD02dmllwpDBcvkzdG48fpy6YW7cCW7ZwgpZn3t70k7s6qowCJWm//PILrKysYG9vDx0dHejo6KBVq1aoWbMmPDw8CjlExhgrfTQ1gYzD6dq1mVeyFUybRj8PHKDmNvbZlixZgsqVK2dbb2hoiMWLF0sQEWPAsWM0Bu3NG2o0DwgovroQamrAgAE0YfTBg4CtLVWGXLmSxq9NnEiJY0EJQQWT2rYFnj6lXnqXL9M8biyP4uJogCLASZoKKVCSVr58eRw6dAj37t3D/v378ccff+DevXs4cOAAypcvX8ghMsZY6dSlC9C7N3U7mjyZvswoqFsX+PJLemDlSkliVDWPHz+GpaVltvXm5uZ48uSJBBGx0m7nTqrimJAAdO8O/PUXzRNW3NTU6Hh09Soljfb2QFISXUSysgLGjKGpG/MjNhYYNIiObykplAxeu0atdiwfjh+nX0bNmtTdkakEjbxu6OLi8tHH/fz85LdX8pcFxhgrFCtW0Pn3r7+ounKfPh9sMH06VXjcvp36RRoZSRCl6jA0NMTNmzdhYWGhsP6ff/5BJSm+GbNSbcWKzAbzb76hLoCamtLGJJNRstitG/D338D8+cDZs1TkaMsWGlP244+UL3zMrVtA//5UnERDg97rxIm5T8LNPiJrV0f+AFVGnpO0oKCgPG0n4z8OxhgrNFZWNPbD3Z0G63frRuM15Nq2pTlxrlyhS9oLFkgWqyoYPHgwJk2aBH19fbRr1w4A4O/vj8mTJ2Pw4MESR8dKCyGoBH7GcFMXF7qtVqD+T0VDJqMumJ06UZK2YAFdTNq2DdixA/j6a5qEOqdxczt2AGPHUutgtWrAvn1c+6jAEhOpxCfAXR1VTIGqO7K84QpajLHCEBcHWFtTQcdFi+gqtQJvb+onVLEi1efW05MkTmVS0ONvcnIynJyc8Mcff0BDg65jpqenY9iwYVi/fj20VGCSJj43KbfUVJoKcft2ur90KTWYl4Rr4Bcv0rxqx47RfZkM+OorYNYs6oWXkECtZVu20ONduwK7dlEVW1ZAR44Ajo5U7vPxY+XK5Fk2RV7dkTHGWPEpWxZYtoxuL14MRER8sEGfPtS36PVr6g/FCkxLSwteXl4IDQ3F7t274ePjg4cPH2Lr1q0qkaAx5RYfD/TtSwmaujr9O8+YUTISNIDGqR09SuPWevemFsF9+2iMWd++9PiWLZRHLFhAyRwnaJ8po6tj376coKkYbkkrQny1kjFWWISg8tsXLgBDh9LVZwXr1lHdfguLzEEepRgff3PHn41yevOGGkTOn6cuzfv20f2S7OZNalnbvz+z8JGhIU2W3amTtLGphJQUwNiYLtD5+QHt20sdEfuEEtWS5unpCUtLS+jo6MDW1hYBAQEf3T4pKQlubm4wNzeHtrY2rKyssPWDK8ceHh6wtraGrq4uqlWrhilTpiAxMVH++Ny5cyGTyRQWY2NjhX0IITB37lyYmppCV1cXX3zxBe7cuVN4b5wxxvJBJqMy1TIZsHs3JWsKRoygS9KPHtE3IlYgAwYMwJIlS7Kt//nnn/HVV19JEBErDSIiaHjp+fNA+fKAr2/JT9AAakHbtw+4fZsOUf37A9evc4JWaPz9KUGrUoWu4jGVImmS5uXlBWdnZ7i5uSEoKAht27ZF9+7dP1rmeODAgTh9+jS2bNmC0NBQ7NmzB3Xq1JE/vnv3bri6umLOnDkICQnBli1b4OXlhZkzZyrsp169eoiMjJQvt27dUnh82bJlWLlyJdauXYurV6/C2NgYXbp0wbt37wr3Q2CMsTyytQVGjaLbkyZRaX45XV0a7AFQhQHuJFEg/v7+6NmzZ7b13bp1w9mzZyWIiKm60FCgVSvgzh2ao/7sWdX7vl23LhUU2b8fMDOTOhoV4uNDP3v3pv6xTKVImqStXLkS3377LUaPHg0bGxt4eHigWrVqWLduXY7bnzhxAv7+/jh27Bg6d+4MCwsLNG/eHK1atZJvc/HiRbRu3RpDhgyBhYUFHBwc8PXXX+PatWsK+9LQ0ICxsbF8qVKlivwxIQQ8PDzg5uaGfv36oX79+tixYwfi4+Px+++/5/p+kpKSEBsbq7AwxlhhWrwYKFcOCAykLz0Kxo2jZO36deDMGUniK+ni4uJyHHumqanJx3RW6K5epYTsyROgdm1qSeNprliepKcDBw7Q7f79pY2FFQnJkrTk5GQEBgbCwcFBYb2DgwMuZOvHQw4fPgw7OzssW7YMZmZmqF27NqZNm4aEhAT5Nm3atEFgYCCuXLkCAAgLC8OxY8eyXRm9f/8+TE1NYWlpicGDByMsLEz+WHh4OF68eKEQm7a2Ntq3b59rbADg7u4OAwMD+VKtWrW8fyCMMZYHhobAnDl0+8cfgbdvszxYuXJmU1tG7W6WL/Xr14eXl1e29Xv37kXdunUliIipqlOngA4dgOhowM4OOHeOhpQylicXLwIvXtBVu44dpY6GFQHJRpZHR0cjLS0NRh9MvGpkZIQXL17k+JywsDCcO3cOOjo6OHDgAKKjozFu3Di8fv1aPi5t8ODBePnyJdq0aQMhBFJTUzF27Fi4urrK99OiRQv89ttvqF27Nv79918sXLgQrVq1wp07d1CpUiX56+cU2+PHj3N9TzNnzlSY9Ds2NpYTNcZYoZswAdi4kbpJzZ8PrFyZ5UEXFyoicuIEjdpv2FCyOEui2bNno3///nj48CE6/vfF5/Tp0/j999+xn8f6sUKyZw8wfDjVfejcmXqt6etLHRUrUTK6Ojo6Alx5ViVJXjjkw8mvhRC5Toidnp4OmUyG3bt3o3nz5ujRowdWrlyJ7du3y1vT/Pz8sGjRInh6euL69evw8fHBkSNHsCDLBK/du3dH//790aBBA3Tu3BlH/5sEcMeOHQWODaDWtnLlyiksjDFW2LS0AA8Pur1mDRASkuXBGjVozjQAWL68uEMrXBKMq+vVqxcOHjyIBw8eYNy4cZg6dSoiIiLw999/w4KbOVgh+OUXYMgQStAGDaKS9ZygsXwRIjNJ466OKkuyJK1y5cpQV1fP1moWFRWVrQUrg4mJCczMzGBgYCBfZ2NjAyEEnj17BoCugjo5OWH06NFo0KAB+vbti8WLF8Pd3R3p6ek57ldPTw8NGjTA/fv3AUBe6TE/sTHGWHHq1o0uoKamAlOmfJDPTJ9OP/fsAZ4+lSS+z3boENCyJSDBOLCePXvi/PnzeP/+PR48eIB+/frB2dkZtra2+d5XfioY+/n5Zas8LJPJcPfuXfk2d+7cQf/+/WFhYQGZTAaPjGydKT0haFLnyZPp/oQJVIqeG0FYvgUFUSVfXV2aEZypJMmSNC0tLdja2sLX11dhva+vr0IhkKxat26N58+fIy4uTr7u3r17UFNTQ9WqVQEA8fHxUPtgMj91dXUIIZDblHBJSUkICQmBiYkJAMDS0hLGxsYKsSUnJ8Pf3z/X2BhjrLitXElf8E6eBI4cyfKAnR3wxReUwa1eLVV4BZOSAkydShN0X7kCrFghSRh///03vvnmG5iammLt2rXo0aNHtgJUn1KQCsYAEBoaqlB9uFatWvLH4uPjUaNGDSxZsiTb1DFMeaWmAv/7H7BoEd1fsIBa1HjuYVYgGa1o3bsDZcpIGwsrOkJCe/fuFZqammLLli0iODhYODs7Cz09PfHo0SMhhBCurq7CyclJvv27d+9E1apVxYABA8SdO3eEv7+/qFWrlhg9erR8mzlz5gh9fX2xZ88eERYWJk6dOiWsrKzEwIED5dtMnTpV+Pn5ibCwMHHp0iXx5ZdfCn19ffnrCiHEkiVLhIGBgfDx8RG3bt0SX3/9tTAxMRGxsbF5fn8xMTECgIiJifmcj4kxxnL1ww9CAEJYWQmRmJjlgWPH6IGyZYV480aq8PLnyRMh7O0pbkCIqVOFSE4u0K4Kcvx9+vSpWLBggbC0tBSGhoZiwoQJQkNDQ9y5c6dAMTRv3lyMGTNGYV2dOnWEq6trjtufOXNGABBv8vj7Mjc3F6tWrcp3XHxuKl4JCUL06UN/0mpqQmzYIHVErMSzsaE/qN27pY6E5VN+jr+SJmlCCPHrr78Kc3NzoaWlJZo2bSr8/f3ljw0fPly0b99eYfuQkBDRuXNnoaurK6pWrSpcXFxEfHy8/PGUlBQxd+5cYWVlJXR0dES1atXEuHHjFE56gwYNEiYmJkJTU1OYmpqKfv36ZTsJp6enizlz5ghjY2Ohra0t2rVrJ27dupWv98YnQsZYUYuNFcLEhM7XS5ZkeSA9XYj69XN4QEkdOyZEpUoUr4GBEAcOfNbu8nv87d69u9DX1xdff/21OHLkiEhNTRVCiAInaUlJSUJdXV34+PgorJ80aZJo165djs/JSNIsLCyEsbGx6Nixo/j7779zfY28JmmJiYkiJiZGvjx9+pTPTcXk7Vsh2renP2ttbSG8vaWOiJV4wcH0B6WpSX9grEQpUUmaKuMkjTFWHHbsoHO2np4QERFZHti+nR4wMfmgmU2JpKQI8eOPma1ntrZCPHz42bvN7/FXXV1dTJkyRdy7d09hfUGTtIiICAFAnD9/XmH9okWLRO3atXN8zt27d8XGjRtFYGCguHDhghg7dqyQyWQKFy+zymuSNmfOHAEg28LnpqL1/LkQjRrRn3W5ckKcOSN1REwlLFxIf1Q9ekgdCSuA/JybuDc0Y4yVcN98A7RoAbx/D2SZbQT4+mvAzAyIjKQKBcomMpLqjy9eTPfHj6fZfGvUKPZQAgIC8O7dO9jZ2aFFixZYu3YtXr58+dn7zU+VYGtra3z33Xdo2rQp7O3t4enpiZ49e2L5Z1bpnDlzJmJiYuTL05JaTKYEefAAaN0a+OcfwMgI8PenYaKMfTZvb/rZr5+0cbAix0kaY4yVcGpqVIofAHbuBC5d+u8BLS3A2Zlu//wzkEuFW0n8/TfQuDF9ey1bFti7F1i7FtDWliQce3t7bNq0CZGRkfjf//6HvXv3wszMDOnp6fD19cW7d+/ytb+CVDDOScuWLeWVhwuKp4cpXtevU4IWHk7XG86fpz91xj5beDhVdlRTA3r1kjoaVsQ4SWOMMRXQrBkwciTdnjQpSz72/fdAuXI0mdqxY5LFJ5eWRjNwd+4MREUBDRoAgYE0YZQSKFOmDEaNGoVz587h1q1bmDp1KpYsWQJDQ0P0yseXooJUMM5JUFCQvPIwUz4pKcDr11QN/eZNYN8+ajGLiqLE7Px5wMpK4iCZ6jhwgH62bw9UqSJtLKzIaUgdAGOMscKxeDGwfz9w9SqwY8d/SVu5clT7++efafnyS+kCjIqivpkZicu331IToK6udDF9hLW1NZYtWwZ3d3f8+eef2Lp1a76e7+LiAicnJ9jZ2cHe3h4bN27EkydPMGbMGADUDTEiIgK//fYbAMDDwwMWFhaoV68ekpOTsWvXLnh7e8M7o3sTaDqY4OBg+e2IiAjcuHEDZcuWRc2aNQvpnau29HTqGhwbS8u7d5m383s/MTHn1/jiC+DgQSDLtK6MfT7u6liqyITIZfIw9tliY2NhYGCAmJgY7l7CGCsWy5fTXNZGRsC9e5SjISICsLSky/6XLtEAtuIWEAAMHgw8f07z+qxbBwwbVmQvpyzHX09PTyxbtgyRkZGoX78+Vq1ahXbt2gEARowYgUePHsHPzw8AsGzZMmzcuBERERHQ1dVFvXr1MHPmTPTo0UO+v0ePHsHS0jLb67Rv316+n09Rls+mMLx8CZw+Dbx9m/ckKy7ug8nfC4GODv2v6esDDg40h6GOTuG+BivlIiMBU1O6/ewZjTdmJU5+jr+cpBUhVToRMsZKhuRk6kF47x4wbRo1ngGgZrXt24H+/am5rbikp1MQbm7U1dHGBvjjD6BevSJ9WT7+5k4VPpv37ykRWraMkq6CUFenxCpj0dfP/f6nHtPULNz3x1g269YB48YBLVsCFy9KHQ0roPwcf7m7I2OMqRAtLWDVKqBnT2D1amD0aMDaGpSxbd8O+PhQ6bni6Br36hUwfDhw9CjdHzoUWL+eCoUwVgCpqcCWLcDcuUBGTZa6delvPL/JlY4OkEuhTcaUD3d1LHU4SWOMMRXTowctx44BU6b8Vy+kXr3MlStXAp6eRRvE5cvAwIHAkydUsXHNGsoY+VsxKwAhaIzXzJlAaCits7SkcZgDB1KxO8ZU1qtXQEZ3Zk7SSg0+rDHGmApatYq6YB0/ntmQhRkz6Oe2bVTEoygIQU14bdtSglazJo2D++47TtBYgZw/D7RpQ99NQ0OBypXpT+zuXRrmyAkaU3mHD1N38UaNuFxoKcKHNsYYU0G1a2dOkTZlCo1VQ7t2VKs/MRH49dfCf9GYGGDAAHrhlBS6fe0aTxLFCiQkBOjdmxK0CxeoCKibG/DwIU0zoaUldYSMFRMfH/rJrWilCidpjDGmombNoiqP9+9TywNkMir9CFCSFh9feC92/TrQtCl9mdDUBH75hSaN4hrkLJ+eP6eG1/r1qQFBXZ2m+3vwAFi48L+KpYyVFu/eAadO0W1O0koVTtIYY0xFlSsHLFlCtxcs+K/QQr9+QI0aNMZh27bPfxEhqBhIq1ZAWBhgbk790yZO5O6NLF9iYqilrGZNYPNmKgzapw9w+zawYUNm9XHGCsW//1IlGmV39Ch1hahdu8ir4jLlwkkaY4ypsGHDgObN6WLszJmgZompU+nBFSs+70vKu3dUsXHsWCApCXB0BIKCqEslY3mUlEQtvVZWVAgkIYFy/nPngAMHgDp1pI6QqZz9+ynrr1ePJtpTZlm7OvKFr1KFkzTGGFNhamrU8xCgCvxXrgAYMYKqL4SHZ34ByK9btygZ27OHEr/ly4FDh4AKFQopcqbq0tPpz8fGhoYxvnpFpfQPHKAErXVrqSNkKikyEvjf/+gP8N49oHNn4JtvqGVN2SQk/FeeFzTHJStVOEljjDEV16IFtagBVHAhXacMMH48rfj5Z+qymB/bt9NOQ0MBMzPA359a5/gqL8ujv/6iHH/IELpWYGxMXRpv36YujvynxIqEEDTA8fVroEkTYMIE+mPbvZuuEKxbR1UUlcWpUzRze7VqgK2t1NGwYsZJGmOMlQJLltAc0pcvAzt3gpI0XV2qvujvn7edxMcDo0YBI0fSFd6uXal7Izd5sDy6cYP+bLp0oVoz+vo0XvLBA/rurMGzt7KitH07cOQIlQb97Teav/HKFSp6FBMDjBtHfW2DgqSOlHBXx1KNkzTGGCsFTEyA2bPptqsr8E6nCiVbALBs2ad3cPcutZ5t20Z9KBcupG44VaoUXdBMZTx+DDg50XfhU6eoAOikSVROf9YsQE9P6giZynvyJHNekgULqHwoANjZUaL2yy901eDKFVo3ZQqNu5VKSgqVNwW4q2MpxUkaY4yVEpMnU+W8Fy8ox4KLCyVcx49TP7Pc/P47fWm5fZtq+v/1F5Xh41mE2Se8ekU9YWvXBnbtot5mgwfTHGirV3OOz4pJejrw7bdAbCxgb59ZPCmDujpVpL17Fxg0iLb38KABk/v3579LeGE4cwZ4+xYwNKTWPVbq8BmWMcZKCW1tYNUqur1qFXA/3Spz3p3ly7M/ITGRKjcOHUrjIr74gvqrdehQXCGzEiohAVi6lCo2rlxJFcQ7dgSuXqViIVZWUkfISpX16+nikq4usGMHJWU5MTUF9u4FTpygP9KICOCrr4CePWmKkeKU0dWxT5/c42UqjZM0xhgrRXr2BLp1o540Li4AZsygB37/HXj2LHPDhw/p6u369TQWYtYs+pJjbCxJ3KxkSEujHrG1a1O32pgYoGFDaqz96y9qkGWsWD18CEyfTreXLgVq1fr0c7p2pQq2s2fT+LXjx6lc/+LFdMWhqKWlUZlTgLs6lmKcpDHGWCkik1ErmoYGjZ8/8aoZ0L49ZW2rV9NGPj40eCgoiEr1Hz9OYzj4ai7LhRD099SoEdWWefYMqF6dGi2uX6cLA1z3gBW7tDSaciQ+nnoAZFS1zQtdXWD+fODmTWoGTkykbt6NGwN+fkUU8H8uXACiooDy5akHAyuVOEljjLFSpk4dKtoA0Dj6FOf/rjJv2EAlqfv3p7EbrVtTota1q2SxMuV3+TJ9j3R0BO7coanyli+nGRqGDePcnknIw4Mm3StbFti6tWDjaK2tqRl41y4aHxYSQgnf8OGUSBWFjK6Ojo7UksdKJU7SGGOsFPrpJ/q+ERoK/HK/O1C3LlUy+/VX2mD6dBq4XrWqtIEypXX/Pg3XadkSOHuWxjzOmEG9y6ZOBXR0pI6QlWrBwdTyBVD3AQuLgu9LJqOxuXfv0jhdmYxK+NepA2zaRIVGCosQmUkad3Us1SRP0jw9PWFpaQkdHR3Y2toiICDgo9snJSXBzc0N5ubm0NbWhpWVFbZu3aqwjYeHB6ytraGrq4tq1aphypQpSExMlD/u7u6OZs2aQV9fH4aGhujTpw9CQ0MV9jFixAjIZDKFpWXLloX3xhljTEIGBjS8AgDmL1RDzLiZdKd8eeDQISrLr6kpWXxMef37L/Uaq1uXCt/JZNSj7P59GvJToYLUEbJSLzWVWrqSkoDu3amyY2GoUAHw9AQuXqRuj2/e0AR/bdpQt8jCEBhI0wXo6QEODoWzT1YiSZqkeXl5wdnZGW5ubggKCkLbtm3RvXt3PHnyJNfnDBw4EKdPn8aWLVsQGhqKPXv2oE6dOvLHd+/eDVdXV8yZMwchISHYsmULvLy8MHPmTPk2/v7+GD9+PC5dugRfX1+kpqbCwcEB79+/V3itbt26ITIyUr4cO3as8D8ExhiTyMiRVMghNhZwuf4NtZwFBwO9ekkdGlNS+/bRNA6envQ9uEcP4J9/qFhItWpSR8fYf5YsAa5do4tOmzcX/oDIFi2oVOmqVdSV8uJFGsc7bRoQF/d5+85oRevRg8bFsVJLJoQUkz+QFi1aoGnTpli3bp18nY2NDfr06QN3d/ds2584cQKDBw9GWFgYKlasmOM+J0yYgJCQEJw+fVq+burUqbhy5UqurXQvX76EoaEh/P390a5dOwDUkvb27VscPHgwz+8nKSkJSUlJ8vuxsbGoVq0aYmJiUK5cuTzvhzHGisvFi1TEUSbLnMNVFcTGxsLAwICPvzn4nM8mOBho0ACwtaXGVq5pwJTOjRtAs2Z0FWHXLuqmWJQiImhw7/79dL9qVWDNGqB37/wnh0JQF8p792iuisGDCz1cJq38HH8la0lLTk5GYGAgHD5oynVwcMCFCxdyfM7hw4dhZ2eHZcuWwczMDLVr18a0adOQkJAg36ZNmzYIDAzElStXAABhYWE4duwYevbsmWssMTExAJAt8fPz84OhoSFq166N7777DlGfGCDq7u4OAwMD+VKNLysyxpScvT3wzTf03WDSJGnmbGUlR926wKVLmcVCGFMqSUnUzTE1leaAHDKk6F/TzAz44w/g6FHA0pJKm/btS0na48f521dwMCVoWlrUksZKNcmStOjoaKSlpcHIyEhhvZGREV68eJHjc8LCwnDu3Dncvn0bBw4cgIeHB/bv34/xWUqqDh48GAsWLECbNm2gqakJKysrdOjQAa6urjnuUwgBFxcXtGnTBvXr15ev7969O3bv3o2///4bK1aswNWrV9GxY0eFlrIPzZw5EzExMfLl6dOn+flIGGNMEkuX0vCHixfpwjNjH9OsGZfTZ0oqo2R+lSrAunXF+4faowdw+zYVK9HUBP78k65qLF1KU5zkhbc3/XRwALgHQKkneeEQ2Qf/QEKIbOsypKenQyaTYffu3WjevDl69OiBlStXYvv27fLWND8/PyxatAienp64fv06fHx8cOTIESxYsCDHfU6YMAE3b97Enj17FNYPGjQIPXv2RP369eHo6Ijjx4/j3r17OHr0aK7vRVtbG+XKlVNYGGNM2ZmaZhZBmzw5/xd/GWNMcpcv01g0AFi/nsrXFrcyZYCFC2mgZvv2ND+bqyvQpAnwicJ4ADLHo/XrV7RxshJBsiStcuXKUFdXz9ZqFhUVla11LYOJiQnMzMxgYGAgX2djYwMhBJ49ewYAmD17NpycnDB69Gg0aNAAffv2xeLFi+Hu7o70D0qkTpw4EYcPH8aZM2dQ9RNlpk1MTGBubo779+8X5O0yxphSmzqVWkjevAEGDQKSk6WOiDHG8ighgbo5pqfTGDSpkxwbGyrEtGMHterduQO0a0dVJqOjc37Ow4eU3Kmrc/EmBkDCJE1LSwu2trbw9fVVWO/r64tWrVrl+JzWrVvj+fPniMtSOefevXtQU1OTJ1nx8fFQ+2CyQnV1dQghkFEjRQiBCRMmwMfHB3///TcsLS0/Ge+rV6/w9OlTmJiY5Ot9MsZYSaClRZX7ypenC9JZCuIyxphyc3OjSR9NTalohzKQyWg297t3qUw/QBNq16lDPz+cWy2jFe2LL4BKlYo1VKacJO3u6OLigs2bN2Pr1q0ICQnBlClT8OTJE4wZMwYAjfEaNmyYfPshQ4agUqVKGDlyJIKDg3H27FlMnz4do0aNgu5/ZUodHR2xbt067N27F+Hh4fD19cXs2bPRq1cvqKurAwDGjx+PXbt24ffff4e+vj5evHiBFy9eyLtMxsXFYdq0abh48SIePXoEPz8/ODo6onLlyujbt28xf0qMMVY8LCyA7dvp9sqVNF0aY4wpNX9/wMODbm/erHwT9VWsCGzYAJw/T6VRX72iFrX27WkMWwbu6sg+oCHliw8aNAivXr3C/PnzERkZifr16+PYsWMwNzcHAERGRirMmVa2bFn4+vpi4sSJsLOzQ6VKlTBw4EAsXLhQvs2sWbMgk8kwa9YsREREoEqVKnB0dMSiRYvk22SU/P/ig9JU27Ztw4gRI6Curo5bt27ht99+w9u3b2FiYoIOHTrAy8sL+vr6hf45pKWlISWvg0pZsdHU1JQn9oyVFr17A1Om0PQ/I0YAQUGUvDHGmNKJi6MJH4UARo+miauVVatWNFH1L78Ac+YA587RWLWpUylpu3SJtuvTR9IwmfKQdJ40VfepuRCEEHjx4gXevn1b/MGxPClfvjyMjY1zLWbDmCpKTqbhE5cvA82b03h3LS2po8ofnictd/zZMJUxdiwVCTE3p6qOJeXv+elTqtJ04ADd19EBEhMpkTt/XtrYWJHKz/FX0pa00i4jQTM0NESZMmU4EVAiQgjEx8fL58bjsYisNNHSAry86CLvlSvADz9QyxpjjCmNkycpQQOAbdtKToIGANWqUffGP/8EJk7MLKnLXR1ZFpykSSQtLU2eoFXiAaJKKWOcY1RUFAwNDbnrIytVzM2pMFmvXjTco107mp+VMaZkhKDqhrGxwLt39DPjNgB07kyl4VXJ27fURRCgJKdDB0nDKTBHR6BjR5o6IDQ08z0xBk7SJJMxBq2Mqh04VUzG7yclJYWTNFbqODrScIkVK2jYR+PGQB6K4TLG8iIlJXtilVOildtjWe+npeX+Og0bUre6GjWK770VtcmTgYgIoFatzLnRSio9PSCXuXxZ6cZJmsS4i6Ny498PK+3c3WmIxKVLwMCBNNZdW1vqqBhTEunpwMuXlDA8e0Y/X73KW6KVmFi4schkgL4+dfvL+PnwIY3VataM+jB37ly4rymFQ4eA334D1NSoHC1f7GYqipM0xhhjudLUpO92jRsD164BM2YAq1dLHRVjxSA5GXj+XDEB+/Dn8+fUIvY5dHUVE6uM5WP3c3pMT48Sl6wiImic05UrQNeuwLJlgIsLJXQlUXR05pxj06ZRoQ3GVBQnaYwxxj6qenW6cO3oSNWj27fn8e2shIuNVUy2ckrA/isc9UkyGWBkBFStCpiZAZUrAwYGeUus9PXpSkhRMTOjecTGjaPiGtOmAdevA5s2lcwWqHHj6PdSrx4wb57U0TBWpDhJY5KysLCAs7MznJ2dpQ6FMfYRX34JTJ8O/PwzMGoUtayp0hAXpiJy6n6YUwKWUVTjU7S0KNHJSMA+/GlmBpiYFG2i9bl0dIAtWwBbW8DZGfj9dyAkhMap/TcvbYng5QX88QegoUFVjXR0pI6IsSLFSRrLty+++AKNGzeGh4fHZ+/r6tWr0NPT+/ygGGNFbtEiGp924QKNTzt/nsenMYlduACsXQs8eUIJWH66HxoYfDoBq1y55HYNzEomA8aPB+rXB776imapt7OjpOeLL6SO7tMiI6kVDQDc3CjhZEzFcZLGCp0QAmlpadDQ+PSfV5UqVYohIsZYYdDUBPbupVa0wEDqObVmjdRRsVLr0CFg0CAgKUlxfUb3w08lYGXLShO3lNq3p8GlfftSt8fOnYGVK6mMvbImo0LQOLTXr2nyRjc3qSNirFiofXoTVlyEAN6/L/5FiLzHOGLECPj7+2P16tWQyWSQyWTYvn07ZDIZTp48CTs7O2hrayMgIAAPHz5E7969YWRkhLJly6JZs2b466+/FPZnYWGh0CInk8mwefNm9O3bF2XKlEGtWrVw+PDhQvqEGWOfq1o1YOdOur12LbB/v7TxKDtPT09YWlpCR0cHtra2CAgIyHVbPz8/+XE163L37l2F7by9vVG3bl1oa2ujbt26OHDgQFG/DeWzaxfQvz8laI6OwL591Kr2+DGti4ykZOTgQeDXX4GZM4Fhw2hOKmvr0pmgZahencq0fvMNle6fPJnm2CjsapOFZft24MgR6nr622/K3bWUsULESZoSiY+n80ZxL/HxeY9x9erVsLe3x3fffYfIyEhERkaiWrVqAIAZM2bA3d0dISEhaNiwIeLi4tCjRw/89ddfCAoKQteuXeHo6IgnT5589DXmzZuHgQMH4ubNm+jRoweGDh2K169ff85HyxgrRD16AD/8QLe//ZaqfLPsvLy84OzsDDc3NwQFBaFt27bo3r37J4+BoaGh8uNrZGQkatWqJX/s4sWLGDRoEJycnPDPP//AyckJAwcOxOXLl4v67SgPT0/AyYkSjOHDAR8f6sJnb08JCH+J/zRdXUp4Vq0C1NVpjFe7dtRlVJk8eULj6ABg/nzqrslYaSFYkYmJiREARExMTLbHEhISRHBwsEhISJCvi4sTgtq1ineJi8vf+2rfvr2YPHmy/P6ZM2cEAHHw4MFPPrdu3bpizZo18vvm5uZi1apV8vsAxKxZs7J8JnFCJpOJ48eP5y/IQpLT74kxJkRyshCtW9MxpEkTIZTtX+Rjx9/i0rx5czFmzBiFdXXq1BGurq45bp9xLH3z5k2u+xw4cKDo1q2bwrquXbuKwYMH5/qcxMREERMTI1+ePn0q+WdTIOnpQixenHnymjhRiLQ0qaMq+f76S4hKlegzNTQUIiBA6ohIWpoQnTtTXC1bCpGaKnVEjH22/JybuCVNiZQpA8TFFf9SWFV47ezsFO6/f/8eM2bMQN26dVG+fHmULVsWd+/e/eRV5IYNG8pv6+npQV9fH1F5LYXMGCsWGePTKlWiGgRTp0odkXJJTk5GYGAgHBwcFNY7ODjgwoULH31ukyZNYGJigk6dOuHMmTMKj128eDHbPrt27frRfbq7u8PAwEC+ZPR+KFGEAFxdgR9/pPs//UQT9n04LxjLv06dgKtXgUaNqLx9hw7AunX5GwtRFNavB/76i1r9duygFj/GShE+uikRmYzmoizupbDGCn9YpXH69Onw9vbGokWLEBAQgBs3bqBBgwZITk7+6H40P+iqIpPJkJ6eXjhBMsYKTdWqmePTPD1pWBAj0dHRSEtLg5GRkcJ6IyMjvHjxIsfnmJiYYOPGjfD29oaPjw+sra3RqVMnnD17Vr7Nixcv8rVPAJg5cyZiYmLky9OnTz/jnUkgLQ0YM4YmYgaAFStojixlLXRREllaUrnWQYOA1FSqpPj999mLshSXhw9pzg8AWLoUqF1bmjgYkxBXd2T5pqWlhbS0tE9uFxAQgBEjRqBv374AgLi4ODx69KiIo2OMFafu3akmg7s7MHo00LQpULOm1FEpD9kHiYQQItu6DNbW1rC2tpbft7e3x9OnT7F8+XK0a9euQPsEAG1tbWiX1LkSUlKo4MfevdRqtnEjDYRkhU9PD9izh8rbu7oCmzcDt28D3t6AqWnxxZGWBowYQQPmO3SgqQMYK4W4JY3lm4WFBS5fvoxHjx4hOjo611aumjVrwsfHBzdu3MA///yDIUOGcIsYYypo/nygTRuaH/irr5S3SFxxqly5MtTV1bO1cEVFRWVrCfuYli1b4v79+/L7xsbGn73PEiMhgUrF792b2b+WE7SiJZNRC9axY0D58sClS5S0XbxYfDF4eFD1ybJlga1buUsrK7X4L5/l27Rp06Curo66deuiSpUquY4xW7VqFSpUqIBWrVrB0dERXbt2RdOmTYs5WsZYUdPQoO/PlSsDN24ALi5SRyQ9LS0t2NrawtfXV2G9r68vWrVqlef9BAUFwcTERH7f3t4+2z5PnTqVr32WCLGxQLduwNGjNCbp8GG6AsCKR9euNE6tXj3gxQuaX23z5qJ/3eDgzHnQVq0CLCyK/jUZU1ZFXsakFMtvdUemfPj3xFjenTiRWXhv715pY1GG6o579+4VmpqaYsuWLSI4OFg4OzsLPT098ejRIyGEEK6ursLJyUm+/apVq8SBAwfEvXv3xO3bt4Wrq6sAILy9veXbnD9/Xqirq4slS5aIkJAQsWTJEqGhoSEuXbqU57iU4bP5qJcvhbC1pT+kcuWUp9pgafTunRD9+mX+Y48dK0RSUtG8VkqKEHZ29Drdu1M1T8ZUDFd3ZIwxVuy6ds0svjd6NJCll16pNGjQIHh4eGD+/Plo3Lgxzp49i2PHjsHc3BwAEBkZqdATITk5GdOmTUPDhg3Rtm1bnDt3DkePHkW/fv3k27Rq1Qp79+7Ftm3b0LBhQ2zfvh1eXl5o0aJFsb+/IhERQfN1BQZS0+yZM9SXlkmjbFmasX7hQuoKuW4dVYP8SKGaAluyhCYgL18e2LSJC8OwUk8mhNQ1VlVXbGwsDAwMEBMTg3Llyik8lpiYiPDwcFhaWkJHR0eiCNmn8O+JsfxJTaXvcGfPUkXvixept1px+9jxt7RT2s/m4UOgc2fg0SPAzIzKr9epI3VULMPRo8CQIdQV1cwMOHAAaNascPZ94wbtKzUV2LULGDq0cPbLmJLJz/GXW9IYY4wVGg0NKhBXpQrwzz/AlClSR8RKhNu3qcXs0SMqD3ruHCdoyqZnT+DKFfq9REQAbdsC27d//n6TkoDhwylB69uXEkHGGCdpjDHGCpepKV0Ml8mADRsoaWMsV1euUBfHFy+ABg2AgAAuGKGsrK2By5eBXr0ouRo5Epg8maZKKKj584GbN6l76/r13M2Rsf9InqR5enrKu5LZ2toiICDgo9snJSXBzc0N5ubm0NbWhpWVFbZu3aqwjYeHB6ytraGrq4tq1aphypQpSPygJvSnXlcIgblz58LU1BS6urr44osvcOfOncJ504wxpuIcHDKLtH3/PRAaKm08TEmdOUP9Y9+8AVq2BPz8AGNjqaNiH1OuHHV1nDOH7v/yC9ClC/DyZf73dfkyjUUD6IqOoWHhxclYCSdpkubl5QVnZ2e4ubkhKCgIbdu2Rffu3XMt6Q4AAwcOxOnTp7FlyxaEhoZiz549qJOlS8Tu3bvh6uqKOXPmICQkBFu2bIGXlxdmzpyZr9ddtmwZVq5cibVr1+Lq1aswNjZGly5d8O7du6L5MBhjTMXMnQt88QUQFwcMHEjTXjEmd/gwzYYeF0eJmq8vULGi1FGxvFBTo3/wAweouIi/P2BnB1y/nvd9JCRQN8f0dBqDlqVADmMM0pbgb968uRgzZozCujp16ghXV9cctz9+/LgwMDAQr169ynWf48ePFx07dlRY5+LiItq0aZPn101PTxfGxsZiyZIl8scTExOFgYGBWL9+fd7enOAS/KqAf0+MfZ7nz4UwNKSq2t99V3yvq/Rl5iWkFJ/Nrl1CqKvTH0bv3kLwMbbkunNHiFq16Hepo0O/27yYMoWeY2IixOvXRRsjY0qiRJTgT05ORmBgIBwcHBTWOzg44MKFCzk+5/Dhw7Czs8OyZctgZmaG2rVrY9q0aUjIcnm2TZs2CAwMxJUrVwAAYWFhOHbsGHr27Jnn1w0PD8eLFy8UttHW1kb79u1zjQ2grpixsbEKC2OMlWYmJsDu3TTMZNMmus1KOU9PwMkJSEujn/v3A1w9t+SqW5fGFfboASQmAt98A0ybRoVAcuPvD3h40O3Nm4EKFYolVMZKEsmStOjoaKSlpcHIyEhhvZGREV7kMv9GWFgYzp07h9u3b+PAgQPw8PDA/v37MX78ePk2gwcPxoIFC9CmTRtoamrCysoKHTp0gKura55fN+NnfmIDAHd3dxgYGMiXatWq5fHTYIwx1dW5MzB7Nt3+3/+Au3eljYdJyN0dGD+epkaeMIGqA2poSB0V+1zly1P31YyBqCtWUFfWV6+ybxsXRwVHhKAJFXv0KNZQGSspJC8cIvugio8QItu6DOnp6ZDJZNi9ezeaN2+OHj16YOXKldi+fbu8Nc3Pzw+LFi2Cp6cnrl+/Dh8fHxw5cgQLFizI9+vmJzYAmDlzJmJiYuTL06dPP/7mSykLCwt4ZFxBA33OBw8ezHX7R48eQSaT4caNG0UeG2OsaPz0E9ChA/D+PfDVV0B8vNQRsWIlBODqmjnb+axZVHBCTfKvIaywqKvTpNd//AHo6dE8d82a0VwcWU2fDoSHA9WrUzLHGMuRZEfHypUrQ11dPVvLVFRUVLYWrAwmJiYwMzODgYGBfJ2NjQ2EEHj27BkAYPbs2XBycsLo0aPRoEED9O3bF4sXL4a7uzvS09Pz9LrG/1WWyk9sAHWJLFeunMLCPi0yMhLdu3cv1H2OGDECffr0KdR9MsYKTl0d+P13wMiIpsSaNEnqiFixSUsDxo4Fli6l+z//DCxYwKXWVdWAATSLfY0alIy1agXs20ePnTxJZfYBYNs2qhTJGMuRZEmalpYWbG1t4evrq7De19cXrVq1yvE5rVu3xvPnzxEXFydfd+/ePaipqaFq1aoAgPj4eKh9cGVOXV0dQggIIfL0upaWljA2NlbYJjk5Gf7+/rnGxgrO2NgY2traUofBGCtixsaZ49O2bAF27pQ6IlbkUlJo3NmGDfSL37iRxisx1dagAXD1KpXmj48HBg0Cpk4Fvv2WHp84EejYUdoYGVN2RVrC5BP27t0rNDU1xZYtW0RwcLBwdnYWenp64tGjR0IIIVxdXYWTk5N8+3fv3omqVauKAQMGiDt37gh/f39Rq1YtMXr0aPk2c+bMEfr6+mLPnj0iLCxMnDp1SlhZWYmBAwfm+XWFEGLJkiXCwMBA+Pj4iFu3bomvv/5amJiYiNjY2Dy/v3xXd0xPFyIurviX9PQ8v6f169cLU1NTkZaWprDe0dFRDBs2TDx48ED06tVLGBoaCj09PWFnZyd8fX0VtjU3NxerVq2S3wcgDhw4IL9/+fJl0bhxY6GtrS1sbW2Fj4+PACCCgoKEEEKkpqaKUaNGCQsLC6GjoyNq164tPDw85M+fM2eOAKCwnDlzRgghxLNnz8TAgQNF+fLlRcWKFUWvXr1EeHh4ru+XqzsyVvjmzqWibmXKCBEcXDSvoRQVDJVUsX028fFC9OxJv2wNDSH27i3a12PKJyVFiOnT6W8gY6lVS4j376WOjDFJ5Of4K2mSJoQQv/76qzA3NxdaWlqiadOmwt/fX/7Y8OHDRfv27RW2DwkJEZ07dxa6urqiatWqwsXFRcTHx8sfT0lJEXPnzhVWVlZCR0dHVKtWTYwbN068efMmz68rBJXhnzNnjjA2Nhba2tqiXbt24tatW/l6b/lO0uLiFA9kxbXExeX5Pb169UpoaWmJv/76S77u9evXQktLS5w8eVLcuHFDrF+/Xty8eVPcu3dPuLm5CR0dHfH48WP59h9L0uLi4kSVKlXEoEGDxO3bt8Wff/4patSooZCkJScni59++klcuXJFhIWFiV27dokyZcoILy8vIQQl8wMHDhTdunUTkZGRIjIyUiQlJYn379+LWrVqiVGjRombN2+K4OBgMWTIEGFtbS2SkpJyfL+cpDFW+FJThejUiQ4/9eoVzfc1TtJyVyyfTUyMEO3bZ5ZlP3q06F6LKb89e4TQ1aVpF86flzoaxiRTopI0VaaKSZoQQvTq1UuMGjVKfn/Dhg3C2NhYpKam5rh93bp1xZo1a+T3P5akbdiwQVSsWFG8z/Ktbd26dQpJWk7GjRsn+vfvL78/fPhw0bt3b4VttmzZIqytrUV6lpbDpKQkoaurK06ePJnjfjlJY6xovHghhLExHYKyHE4KDSdpuSvyzyY6WohmzeiXq68vxAcXQVkp9eyZECEhUkfBmKTyc/zlurfKpEwZKk0rxevmw9ChQ/H999/D09MT2tra2L17NwYPHgx1dXW8f/8e8+bNw5EjR/D8+XOkpqYiISEBT548ydO+Q0JC0KhRI5TJEpO9vX227davX4/Nmzfj8ePHSEhIQHJyMho3bvzRfQcGBuLBgwfQ19dXWJ+YmIiHDx/mKT7GWOEwMqJCIp07A1u3Au3bA8OGSR0V+2zPn9M4pOBgoHJl4MQJwNZW6qiYMjAzkzoCxkoUTtKUiUxGZWuVnKOjI9LT03H06FE0a9YMAQEBWLlyJQBg+vTpOHnyJJYvX46aNWtCV1cXAwYMQHJycp72LYT45Db79u3DlClTsGLFCtjb20NfXx8///wzLl++/NHnpaenw9bWFrtzmE23SpUqeYqPMVZ4OnQA5s6l8vxjxwJ2djQvLiuhwsIo6w4Ppy/kvr6AjY3UUTHGWInESRrLN11dXfTr1w+7d+/GgwcPULt2bdj+d6U0ICAAI0aMQN++fQEAcXFxePToUZ73XbduXezcuRMJCQnQ1dUFAFy6dElhm4CAALRq1Qrjxo2Tr/uwJUxLSwtpaWkK65o2bQovLy8YGhry9AiMKYkffwTOnqUplb76CrhypURcq2IfunOHWtAiIwErK/qFWlhIHRVjjJVYPIskK5ChQ4fi6NGj2Lp1K7755hv5+po1a8LHxwc3btzAP//8gyFDhiA9PT3P+x0yZAjU1NTw7bffIjg4GMeOHcPy5csVtqlZsyauXbuGkydP4t69e5g9ezauXr2qsI2FhQVu3ryJ0NBQREdHIyUlBUOHDkXlypXRu3dvBAQEIDw8HP7+/pg8ebJ8nj3GWPFSVwd27aLy/MHBwIQJUkfE8u3qVaBdO0rQ6tcHAgI4QWOMsc/ESRorkI4dO6JixYoIDQ3FkCFD5OtXrVqFChUqoFWrVnB0dETXrl3RtGnTPO+3bNmy+PPPPxEcHIwmTZrAzc0NSzMmQP3PmDFj0K9fPwwaNAgtWrTAq1evFFrVAOC7776DtbU17OzsUKVKFZw/fx5lypTB2bNnUb16dfTr1w82NjYYNWoUEhISuGWNMQkZGQF79gBqasD27bSwEsLPj+a7ev0aaNEC8PcHTEykjooxxko8mcjLICBWILGxsTAwMEBMTEy2JCAxMRHh4eGwtLSEjo6ORBGyT+HfE2PFZ+FCYPZsQFeXGmfq1Sv4vj52/C3tCu2zOXIEGDAASEqiRO3QIaBs2cILlDHGVEx+jr/cksYYY0wp/PgjDWtKSKDxaVIUu2V5tGcP0LcvJWi9ewNHj3KCxhhjhYiTNMYYY0pBTY3Gp5maAiEhwLhxNJkjUzLr1wNDhwKpqcA33wB//AFwTwPGGCtUnKQxxhhTGoaGmePTdu4Etm2TOiKmYPNmmi9BCGD8eGDHDkBTU+qoGGNM5XCSxhhjTKm0awcsWADUqAE0aiR1NExBt26AuTng5gasWUPZNGOMsULH86RJLD/l6Vnx498PY9JwdaVy/FzzQ8lUrQrcuAGULy91JIwxptI4SZOIlpYW1NTU8Pz5c1SpUgVaWlqQyWRSh8X+I4RAcnIyXr58CTU1NWhpaUkdEmOlipoaJ2hKixM0xhgrcpykSURNTQ2WlpaIjIzE8+fPpQ6H5aJMmTKoXr061LhLD2OMMcYYKyacpElIS0sL1atXR2pqKtLS0qQOh31AXV0dGhoa3MLJGGOMMcaKFSdpEpPJZNDU1IQmV8dijDHGGGOMgas7MsYYY4wxxphS4SSNMcYYY4wxxpQIJ2mMMcYYY4wxpkR4TFoREkIAAGJjYyWOhDHGSpeM427GcZhl4nMTY4xJIz/nJk7SitC7d+8AANWqVZM4EsYYK53evXsHAwMDqcNQKnxuYowxaeXl3CQTfJmxyKSnp+P58+fQ19cvUBn32NhYVKtWDU+fPkU5ntVVAX82uePP5uP488mdKn02Qgi8e/cOpqamPM/hB/jcVHT4s8kdfza5488md6r22eTn3MQtaUVITU0NVatW/ez9lCtXTiX+MIsCfza548/m4/jzyZ2qfDbcgpYzPjcVPf5scsefTe74s8mdKn02eT038eVFxhhjjDHGGFMinKQxxhhjjDHGmBLhJE2JaWtrY86cOdDW1pY6FKXDn03u+LP5OP58csefDcsL/jvJHX82uePPJnf82eSuNH82XDiEMcYYY4wxxpQIt6QxxhhjjDHGmBLhJI0xxhhjjDHGlAgnaYwxxhhjjDGmRDhJY4wxxhhjjDElwkkaY4wxxhhjjCkRTtKUlKenJywtLaGjowNbW1sEBARIHZJScHd3R7NmzaCvrw9DQ0P06dMHoaGhUoellNzd3SGTyeDs7Cx1KEohIiIC33zzDSpVqoQyZcqgcePGCAwMlDosyaWmpmLWrFmwtLSErq4uatSogfnz5yM9PV3q0JgS4nNTdnxeyjs+L2XH56ac8bmJkzSl5OXlBWdnZ7i5uSEoKAht27ZF9+7d8eTJE6lDk5y/vz/Gjx+PS5cuwdfXF6mpqXBwcMD79++lDk2pXL16FRs3bkTDhg2lDkUpvHnzBq1bt4ampiaOHz+O4OBgrFixAuXLl5c6NMktXboU69evx9q1axESEoL/t3c/IU2/ARzHP7Uf/ilEqHAYoejJf4HOgaRih8JDGgSRKGKBJ8E/y0Ekek0FhQiyBhPpUEgeCrJbYqCoB0VdRRcPgXoJ7WKEYDCfbsLYpv5OzwN7v2AHn9Pn9ub5ju8cGRnR6Oionj9/bnsaHEObEqNLp0OX4tGm5GgT/yfNSVVVVfL5fAqFQkdnxcXFunPnjoaHhy0uc8/u7q5ycnI0Nzenuro623Oc8OfPH/l8Pr18+VJPnjxReXm5nj17ZnuWVX19fVpcXOSpfwKNjY3yer2amJg4Ort7967OnTun169fW1wG19Cm06FL8ehSYrQpOdrEN2nO+fv3r1ZXV1VfXx9zXl9fr6WlJUur3LW3tydJunDhguUl7ujs7FRDQ4Nu3rxpe4ozpqen5ff7de/ePeXk5KiiokLj4+O2ZzmhtrZWs7Oz2tjYkCR9+fJFCwsLunXrluVlcAltOj26FI8uJUabkqNN0n+2ByDWr1+/FI1G5fV6Y869Xq9+/vxpaZWbjDEKBoOqra1VWVmZ7TlOePv2rdbW1rSysmJ7ilN+/PihUCikYDCo/v5+LS8vq6enR+np6bp//77teVY9fvxYe3t7KioqksfjUTQa1eDgoFpaWmxPg0No0+nQpXh0KTnalBxt4pLmrDNnzsT8bYyJO0t1XV1d+vr1qxYWFmxPccL29rYCgYA+ffqkjIwM23Occnh4KL/fr6GhIUlSRUWFvn//rlAolPIhnJqa0ps3bzQ5OanS0lJFIhE9fPhQly9f1oMHD2zPg2No0/HoUiy6dDzalBxt4pLmnEuXLsnj8cQ9mdzZ2Yl7gpnKuru7NT09rfn5eV25csX2HCesrq5qZ2dHlZWVR2fRaFTz8/MaGxvTwcGBPB6PxYX25ObmqqSkJOasuLhY7969s7TIHY8ePVJfX5+am5slSVevXtXm5qaGh4dTJoQ4GW06GV2KR5eOR5uSo028k+actLQ0VVZWamZmJuZ8ZmZG1dXVlla5wxijrq4uvX//Xp8/f1ZBQYHtSc64ceOGvn37pkgkcvTx+/1qbW1VJBJJ6RDW1NTE/ST2xsaG8vPzLS1yx/7+vs6ejU2Bx+NJqZ85xsloU3J0KTm6dDzalBxt4ps0JwWDQbW1tcnv9+vatWsKh8Pa2tpSR0eH7WnWdXZ2anJyUh8+fFBWVtbRU93s7GxlZmZaXmdXVlZW3DsQ58+f18WLF1P+3Yje3l5VV1draGhITU1NWl5eVjgcVjgctj3Nutu3b2twcFB5eXkqLS3V+vq6nj59qvb2dtvT4BjalBhdSo4uHY82JUebJBk46cWLFyY/P9+kpaUZn89n5ubmbE9ygqSEn1evXtme5qTr16+bQCBge4YTPn78aMrKykx6eropKioy4XDY9iQn/P792wQCAZOXl2cyMjJMYWGhGRgYMAcHB7anwUG0KR5d+n/oUizalBhtMob/kwYAAAAADuGdNAAAAABwCJc0AAAAAHAIlzQAAAAAcAiXNAAAAABwCJc0AAAAAHAIlzQAAAAAcAiXNAAAAABwCJc0AAAAAHAIlzQAAAAAcAiXNAAAAABwCJc0AAAAAHDIP7bcofk1WDqiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss=history.history['loss']\n",
    "acc=history.history['accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='validate')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_acc,color='r',label='validate')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d3d6d28-2a2c-4e99-984a-38bb6f7d758e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "评论为： The ultimate story of friendship,of hope,and of life,and overcoming adversity.I understand why so many class this as the best film of all time,it isn't mina,but I get it.If you haven't seen it,or haven't seen it for some time,you need to watch it,it's amazing.\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "30",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m预测结果为：\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mdict\u001b[39m[np\u001b[38;5;241m.\u001b[39margmax(pred)])\n\u001b[0;32m     10\u001b[0m test_text\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe ultimate story of friendship,of hope,and of life,and overcoming adversity.I understand why so many class this as the best film of all time,it isn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt mina,but I get it.If you haven\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt seen it,or haven\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt seen it for some time,you need to watch it,it\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms amazing.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 11\u001b[0m display_predict(test_text)\n",
      "Cell \u001b[1;32mIn[19], line 9\u001b[0m, in \u001b[0;36mdisplay_predict\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m      7\u001b[0m pred\u001b[38;5;241m=\u001b[39mmodel\u001b[38;5;241m.\u001b[39mpredict(test_seq)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m评论为：\u001b[39m\u001b[38;5;124m\"\u001b[39m,text)\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m预测结果为：\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;28mdict\u001b[39m[np\u001b[38;5;241m.\u001b[39margmax(pred)])\n",
      "\u001b[1;31mKeyError\u001b[0m: 30"
     ]
    }
   ],
   "source": [
    "dict={0:\"正面评论\",1:\"负面评论\"}\n",
    "def display_predict(text):\n",
    "    token=tf.keras.preprocessing.text.Tokenizer(num_words=4000)\n",
    "    token.fit_on_texts(text)\n",
    "    input_seq=token.texts_to_sequences(text)\n",
    "    test_seq=tf.keras.preprocessing.sequence.pad_sequences(input_seq,padding='post',maxlen=400,truncating='post')\n",
    "    pred=model.predict(test_seq)\n",
    "    print(\"评论为：\",text)\n",
    "    print(\"预测结果为：\",dict[np.argmax(pred)])\n",
    "test_text=\"The ultimate story of friendship,of hope,and of life,and overcoming adversity.I understand why so many class this as the best film of all time,it isn't mina,but I get it.If you haven't seen it,or haven't seen it for some time,you need to watch it,it's amazing.\"\n",
    "display_predict(test_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b1740c-4dc4-4f7f-89b6-4b3efe8fe140",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
